I'm sure you all agree
that machine learning is one of the hottest Trend in today's
market right Gartner predicts that by 2022 there
would be at least 40% of new application development
project going on in the market that would be requiring
machine learning co-developers on their team. It's expected that these project
will generate a revenue of around three point
nine trillion dollar, isn't it cute so
looking at the huge? Upcoming demand of machine
learning around the world. We guys at Eureka have come up and designed a well-structured
machine learning full course for you guys. But before we actually
drill down over there, let me just introduce myself. Hello all I am Atul from Edureka. And today I'll be guiding you through this entire
machine learning course. Well, this course
has been designed in a way that you get the most out of it. So we'll slowly
and gradually start with a beginner level and then
move towards the advanced topic. So without delaying any further, let's start with the agenda
of today's Action on machine learning
course has been segregated into six different module
will start our first module with introduction to
machine learning here. We'll discuss things. Like what exactly
is machine learning how it differs from artificial
intelligence and the planning what is various types
or dead space application and finally we'll end
up first module with a basic demo and python. Okay a second module
focuses on starts and probability here
will cover things like descriptive statistics and
inferential statistics to Bob. Rarity Theory and so on our third module
is unsupervised learning. Well supervised learning is one
of a type of machine learning which focuses mainly on regression and
classification type of problem. It deals with label data sets
and the algorithm which are a part of it
are linear regression logistic regression Napier's
random Forest decision tree and so on. Our fourth module is
on unsupervised learning. Well this module focuses
mainly on dealing with unlabeled data sets and the algorithm
which are a part. Offered or k-means algorithm and a priori algorithm as
a part of fifth module. We have reinforcement
learning here. We are going to discuss
about reinforcement learning and depth on also about Q learning algorithm
finally in the end. It's all about to make
you industry ready. Okay. So here we are going to discuss
about three different projects which are based
on supervised learning and unsupervised learning and reinforcement learning
finally in the end. I tell you about some
of the skills that you need to become
a machine learnings and Jean. Nia okay, and also I
am discussing about some of the important questions that are asked in a
machine-learning interview fine with this we come
to the end of this agenda before you move ahead don't forget to subscribe
to a dareka and press the Bell icon to never miss
any update from us. Hello everyone. This is a toll from Eureka and welcome to today's session
on what is machine learning. As you know, we are living
in a world of humans and machines humans
have been evolving and learning from the past
experience since millions of years on the other hand
the era of machines and robots have just
begun in today's world. These machines are
the rewards are like they need to be program before they actually
follow your instructions. But what if the machine
started to learn on their own and this is where machine learning comes into picture machine
learning is the core of many futuristic technology
advancement in our world. And today you can
see various examples or implementation of
machine learning around us such as Tesla's self-driving
car Apple Siri, Sophia. I do bot and many
more are there. So what exactly
is machine learning? Well Machine learning
is a subfield of artificial intelligence that focuses on
the design of system that can learn from
and make decisions and predictions based
on the experience which is data in the case
of machines machine learning enables computer to act and make data-driven
decisions rather than Being explicitly programmed to carry out a certain
task these programs are designed to learn and improve over time when exposed to new data. Let's move on and discuss one of the biggest confusion
of the people in the world. They think that all
the three of them the AI the machine learning and
the Deep learning all are same, you know, what they are wrong. Let me clarify things for you artificial intelligence
is a broader concept of machines being able to carry
out tasks in a smarter way. It covers anything which enables
the computer to be. Have like humans think of a
famous Turing test to determine whether a computer is
capable of thinking like a human being or not. If you are talking
to Siri on your phone and you get an answer you're
already very close to it. So this was about the artificial
intelligence now coming to the machine learning part. So as I already said
machine learning is a subset or a current application
of AI it is based on the idea that we should be able
to give machine the access to data and let them learn
from done cells. It's a subset
of artificial intelligence. Is that deals with the extraction
of pattern from data set? This means that the machine
can not only find the rules for optimal Behavior, but also can adapt
to the changes in the world many of the algorithms
involved have been known for decades centuries
even thanks to the advances in the computer science
and parallel Computing. They can now scale up
to massive data volumes. So this was about the machine
learning part now coming over to deep learning deep learning
is a subset of machine learning where similar machine learning. Tamar used to train
deep neural network. So as to achieve better
accuracy in those cases where former was not performing
up to the mark, right? I hope now you understood
that machine learning Ai and deep learning
all three are different. Okay moving on ahead. Let's see in general
how a machine learning work. One of the approaches is where the machine learning
algorithm is strained using a labeled
or unlabeled training data set to produce a model new input data is introduced to
the machine learning algorithm and it make prediction
based on the model. The prediction is
evaluated for accuracy. And if the accuracy
is acceptable the machine learning algorithm is deployed. Now if the accuracy
is not acceptable the machine learning
algorithm is strained again, and again with an argument
a training data set. This was just
in high-level example as they are many more factor
and other steps involved in it. Now, let's move on
and subcategorize the Machine learning into three different
types the supervised learning and unsupervised learning
and reinforcement learning and let's see what each
of them are how they work. Work and how each of them is used in the field
of banking Healthcare retail and other domains. Don't worry. I'll make sure that I use enough examples
and implementation of all three of them to give you
a proper understanding of it. So starting with
supervised learning. What is it? So let's see
a mathematical definition of supervised learning
supervised learning is where you have input variables X
and an output variable Y and you use an algorithm
to learn the mapping function from the input to the output. That is y Affects the goal is to approximate
the mapping function. So well that whenever
you have a new input data X you could predict
the output variable. That is why
for that data, right? I think this
was confusing for you. Let me simplify the definition
of supervised learning so we can rephrase
the understanding of the mathematical definition
as a machine learning method where each instances of
a training data set is composed of different input attribute and an expected output
the input attributes of a training data set can be
of any End of data it can be a pixel of the image. It can be a value
of a data base row or it can even be an audio
frequency histogram right for each input instance and expected output values Associated value can be discreet
representing a category or can be a real or continuous
value in either case. The algorithm learns
the input pattern that generate the
expected output now once the algorithm is strain, it can be used to predict
the correct output of a never seen input. You can see I image
on your screen right in this image. And see that we are feeding
raw inputs as image of Apple to the algorithm as a part
of the algorithm. We have a supervisor
who keeps on correcting the machine or who keeps
on training the machine. It keeps on telling him
that yes, it is a Apple. No, it is not an apple
things like that. So this process keeps on repeating until we
get a final train model. Once the model is ready. It can easily predict
the correct output of a never seen input
in this slide. You can see that we are giving an image
of a green apple to the machine and the Machine can easily
identify it as yes, it is an apple and it is giving
the correct result right? Let me make things
more clearer to you. Let's discuss another
example of it. So in this Slide, the image shows an example of a supervised learning process
used to produce a model which is capable of recognizing
the ducks in the image. The training data set
is composed of labeled picture of ducks and non Ducks. The result of supervised
learning process is a predictor model which is capable
of associating a label duck. Or not duck to the new image
presented to the model. Now one strain, the resulting predictive
model can be deployed to the production environment. You can see a mobile app. For example once deployed
it is ready to recognize the new pictures right now. You might be wondering
why this category of machine learning is named
as supervised learning. Well, it is called
a supervised learning because the process
of an algorithm learning from the training data
set can be thought of as a teacher supervising
the learning process if we know the correct answers. I will go Rhythm
iteratively makes while predicting on
the training data and is corrected by
the teacher the learning stops when the algorithm achieves an
acceptable level of performance. Now, let's move on and see some of the popular supervised
learning algorithm. So we have linear
regression random forest and support Vector machines. These are just
for your information. We will discuss
about these algorithms in our next video. Now, let's see some
of the popular use cases of supervised learning so we have Donna codon
or any other speech Automation in your mobile
phone trains using your voice and one strain it start working
based on the training. This is an application
of supervised learning suppose. You are telling
OK Google call Sam or you say Hey Siri call
Sam you get an answer to it and action is performed and automatically
a call goes to Sam. So these are just an example of supervised learning next
comes the weather up based on some
of the prior knowledge like when it is sunny
the temperature is high. Fire when it is cloudy humidity
is higher any kind of that they predict the parameters
for a given time. So this is also an example
of supervised learning as we are feeding the data
to the machine and telling that whenever it is sunny. The temperature should be higher
whenever it is cloudy. The humidity should be higher. So it's an example
of supervised learning. Another example is
biometric attendance where you train the machine
and after couple of inputs of your biometric identity
beat your thumb your iris or yellow or anything once trained Machine gun
validate your future input and can identify you next comes
in the field of banking sector in banking sector supervised learning is used
to predict the credit worthiness of a credit card holder by building a machine
learning model to look for faulty attributes
by providing it with a data on deliquent and non-delinquent customers. Next comes the healthcare sector
in the healthcare sector. It is used to predict
the patient's readmission rates by building a regression model by providing data on the patients treatment
Administration and readmissions to show variables that best correlate
with readmission. Next comes the retail sector
and Retail sector. It is used to
analyze the product that a customer by together. It does this by building
a supervised model to identify frequent itemsets and Association rule
from the transactional data now, lets learn about
the next category of machine learning the
unsupervised part mathematically unsupervised learning is where you only have Put data X and no
corresponding output variable. The goal for unsupervised
learning is to model the underlying structure or distribution in the data in order to learn
more about the data. So let me rephrase you
this in simple terms in unsupervised learning
approach the data instances of a training data
set do not have an expected output Associated to them instead unsupervised learning algorithm
detects pattern based on innate characteristics of the input data an example
of machine learning tasks. Ask that applies unsupervised
learning is clustering in this task similar data
instances are grouped together in order to identify clusters
of data in this slide. You can see that initially
we have different varieties of fruits as input. Now these set of fruits as
input X are given to the model. Now, what is the model
is trained using unsupervised learning algorithm. The model will create clusters
on the basis of its training. It will grip the similar fruits
and make their cluster. Let me make things
more clearer to you. Let's take another
example of it. So in this Slide the image
below shows an example of unsupervised learning process
this algorithm processes an unlabeled training data set and based on
the characteristics. It grips the picture into three different clusters
of data despite the ability of grouping similar
data into clusters. The algorithm is not capable
to add labels to the crow. The algorithm only knows which
data instances are similar, but it cannot identify
the meaning of this group. So, Now you might be wondering
why this category of machine learning is named
as unsupervised learning. So these are called as unsupervised learning because
unlike supervised learning ever. There are no correct answer and there is no teacher
algorithms are left on their own to discover and present the interesting
structure in the data. Let's move on and see some of the popular unsupervised
learning algorithm. So we have here
k-means apriori algorithm and hierarchical clustering now, let's move on and see
some of the examples of Is learning suppose a friend
invites you to his party and where you meet
totally strangers. Now, you will classify them
using unsupervised learning as you don't have
any prior knowledge about them and this classification
can be done on the basis of gender age group
dressing education qualification or whatever way you
might like now why this learning is different
from supervised learning since you didn't use
any pasta prior knowledge about the people you kept on
classifying them on the go as they kept on coming you
kept on classifying them. Yeah, this category
of people belong to this group this category of people belong
to that group and so on. Okay, let's see
one more example. Let's suppose you have never
seen a football match before and by chance you watch
a video on the internet. Now, you can easily classify
the players on the basis of different Criterion, like player wearing
the same kind of Jersey are in one class player
wearing different kind of Jersey aren't different class or you can classify
them on the basis of their playing style
like the guys are attacker. So he's in one class. He's a Defender
he's Another class or you can classify them. Whatever Way You
observe the things so this was also an example
of unsupervised learning. Let's move on and see how unsupervised learning
is used in the sectors of banking Healthcare undertale. So starting at banking sector. So in banking sector it
is used to segment customers by behavioral characteristic
by surveying prospects and customers to develop
multiple segments using clustering and
Healthcare sector. It is used to categorize the MRI
data by normal or abnormal. Ages it uses deep learning
techniques to build a model that learns from different
features of images to recognize a different pattern. Next is the retail sector
and Retail sector. It is used to recommend
the products to customer based on their past purchases. It does this by building
a collaborative filtering model based on the past
purchases by them. I assume you guys now have a proper idea of
what unsupervised learning means if you have any slightest doubt don't hesitate and add your
doubt to the I'm in section. So let's discuss the third and the last type
of machine learning that is reinforcement learning. So what is
reinforcement learning? Well reinforcement learning is a type of machine
learning algorithm which allows software agents and machine to automatically
determine the ideal Behavior within a specific context
to maximize its performance. The reinforcement learning
is about interaction between two elements the environment and
the learning agent the learning agent leverages
to mechanism namely exploration. And exploitation when
learning agent acts on trial and error basis, it is termed as exploration and when it acts based
on the knowledge gained from the environment, it is referred
to as exploitation. Now this environment rewards
the agent for correct actions, which is reinforcement signal
leveraging the rewards obtain the agent improves its environment
knowledge to select the next action in this image. You can see
that the machine is confused whether it is an apple
or it's not an apple then the Sheena's chain
using reinforcement learning. If it makes correct decision. It get rewards point for it and in case of wrong it gets
a penalty for that. Once the training is done. Now. The machine can easily identify
which one of them is an apple. Let's see an example here. We can see that we have an agent who has to judge
from the environment to find out which of the two is
a duck the first task he did is to observe
the environment next. We select some action
using some policy. It seems that the machine
has made a wrong decision. Bye. Choosing a bunny as a duck. So the machine will
get penalty for it. For example -
50.4 a wrong answer right now. The machine will
update its policy and this will continue till the machine gets
an optimal policy from the next time machine will
know that bunny is not a duck. Let's see some of the use cases
of reinforcement learning but before that lets see how Pavlo trained his dog
using reinforcement learning or how he applied the reinforcement method
to train his dog. Babu integrated learning in four stages initially
Pavlo gave me to his dog and in response to the meet
the dog started salivating next what he did he created a sound with the bell for this the dog
did not respond anything in the third part it
tried to condition the dog by using the bell and then giving him
the food seeing the food the dog started salivating
eventually a situation came when the dog started salivating
just after hearing the Bell even if the food was not given to him
as the The dog was reinforced that whenever the master
will ring the bell he will get the food now. Let's move on and see how reinforcement learning
is applied in the field of banking Healthcare
and Retail sector. So starting with
the banking sector in banking sector reinforcement
learning is used to create a next best offer model for a call center
by building a predictive model that learns over time as user accept or reject offer
made by the sales staff fine now in healthcare sector it
is used to allocate the scars. Resources to handle
different type of er cases by building
a Markov decision process that learns treatment strategies
for each type of er case next and the last comes
in retail sector. So let's see how reinforcement learning
is applied to retail sector and Retail sector. It can be used to
reduce excess stock with Dynamic pricing by building
a dynamic pricing model that are just the price based on customer response
to the offers. I hope by now you have attained
some understanding of what is machine learning
and you are ready to move. Move ahead. Welcome to today's topic
of discussion on AI versus machine learning
versus deep learning. These are the term
which have confused a lot of people and if you
two are one among them, let me resolve it for you. Well artificial intelligence
is a broader umbrella under which machine learning and deep learning come you
can also see in the diagram that even deep learning is a subset of machine
learning so you can say that all three of them The AI
and machine learning and deep learning are just
the subset of each other. So let's move on and understand how exactly the differ
from each other. So let's start
with artificial intelligence. The term artificial intelligence was first coined
in the year 1956. The concept is pretty old, but it has gained
its popularity recently. But why well, the reason is earlier we had
very small amount of data the data we had was not enough
to predict the Turret result but now there's
a tremendous increase in the amount of data statistics suggest that by 2020
the accumulated volume of data will increase from 4.4 zettabyte stew
roughly around 44 zettabytes or 44 trillion jeebies of data along with such
enormous amount of data. Now, we have more
advanced algorithm and high-end computing
power and storage that can deal with such large
amount of data as a result. It is expected that 70% of The price
will Implement a i over the next 12 months which is up from 40 percent
in 2016 and 51 percent in 2017. Just for your understanding. What does AI well, it's nothing but a technique that enables the machine
to act like humans by replicating the behavior
and nature with AI it is possible for machine to learn
from the experience. The machines are just
their responses based on new input there by performing human-like tasks
artificial intelligence can be and to accomplish specific tasks by processing
large amount of data and recognizing pattern in them. You can consider that building an artificial
intelligence is like Building a Church the first church
took generations to finish. So most of the workers
were working in it never saw the final outcome those working on it took pride
in their craft building bricks and chiseling stone that was going to be placed
into the great structure. So as AI researchers, we should think of ourselves
as humble brick makers was job. It's just study how to build components
example Parts is planners or learning algorithm
or Etc anything that someday someone
and somewhere will integrate into the intelligent systems
some of the examples of artificial intelligence
from our day-to-day life are Apple series chess-playing
computer Tesla self-driving car and many more these examples
are based on deep learning and natural language processing. Well, this was about what is AI
and how it gains its hype. So moving on ahead. Let's Gus about machine
learning and see what it is and why it was the
when introduced well Machine learning came
into existence in the late 80s and the early 90s, but what were the issues
with the people which made the machine learning
come into existence let us discuss them one by one
in the field of Statistics. The problem was how to efficiently train
large complex model in the field of computer science
and artificial intelligence. The problem was
how to train more robust version of AI system while in
the case of Neuroscience. Problem faced by
the researchers was how to design operation
model of the brain. So these were some of the issues which had the largest influence
and led to the existence of the machine learning. Now this machine learning
shifted its focus from the symbolic approaches. It had inherited
from the AI and move towards the methods and model. It had borrowed from statistics
and probability Theory. So let's proceed and see what exactly is
machine learning. Well Machine learning
is a subset of AI which enables the
computer to act and make data-driven decisions
to carry out a certain task. These programs are algorithms
are designed in a way that they can learn
and improve over time when exposed to new data. Let's see an example
of machine learning. Let's say you want
to create a system which tells the expected weight
of a person based on its side. The first thing you do
is you collect the data. Let's see there is how your data looks
like now each point on the graph represent
one data point to start with we can draw a simple line
to predict the weight based on the height for Sample
a simple line W equal x minus hundred with W
is waiting kgs and edges hide and centimeter this line can
help us to make the prediction. Our main goal is
to reduce the difference between the estimated value
and the actual value. So in order to achieve it, we try to draw a straight line
that fits through all these different points
and minimize the error. So our main goal is
to minimize the error and make them as small as
possible decreasing the error or the difference between
the actual value and estimated. Value increases the performance
of the model further on the more data points. We collect the better. Our model will become we
can also improve our model by adding more variables and creating different
production lines for them. Once the line is created. So from the next time
if we feed a new data, for example height
of a person to the model, it would easily predict the data
for you and it will tell you what has predicted
weight could be. I hope you got
a clear understanding of machine learning. So moving on ahead. Let's learn about deep learning
now what is deep learning? You can consider deep learning
model as a rocket engine and its fuel is
its huge amount of data that we feed to
these algorithms the concept of deep learning is not new, but recently it's
hype as increase and deep learning
is getting more attention. This field is a particular kind
of machine learning that is inspired by the functionality of
our brain cells called neurons which led to the concept
of artificial neural network. It simply takes the data connection between all
the artificial neurons and adjust them according
to the data pattern. More neurons are added at the size of the data is large
it automatically features learning at multiple
levels of abstraction. Thereby allowing a system to learn complex function
mapping without depending on any specific algorithm. You know, what no one
actually knows what happens inside a neural network
and why it works so well, so currently you can call
it as a black box. Let us discuss some
of the example of deep learning and understand it
in a better way. Let me start with
in simple example and explain you how things And
at a conceptual level, let us try and understand how you would recognize
a square from other shapes. The first thing
you do is you check whether there are four lines
associated with a figure or not simple concept, right? If yes, we further check if they are connected
and closed again a few years. We finally check
whether it is perpendicular and all its sides
are equal, correct. If everything fulfills. Yes, it is a square. Well, it is nothing but
a nested hierarchy of Concepts. What we did here we
took a complex task of identifying a square and this case and broken
into simpler tasks. Now this deep learning
also does the same thing but at a larger scale, let's take an example
of machine which recognizes the animal the task
of the machine is to recognize whether the given image is
of a cat or a dog. What if we were asked to resolve
the same issue using the concept of machine learning
what we would do first. We would Define
the features such as check whether the animal has
whiskers or not a check. The animal has pointed ears or not or whether its tail
is straight or curved in short. We will Define
the facial features and let the system identify which
features are more important in classifying a
particular animal now when it comes to deep learning
it takes this to one step ahead deep learning automatically
finds are the feature which are most important
for classification compare into machine learning where we had to manually give
out that features by now. I guess you have understood
that AI is the bigger picture and machine learning and
deep learning are it's apart. So let's move on and focus our discussion
on machine learning and deep learning the easiest
way to understand the difference between the machine learning
and deep learning is to know that deep learning is machine
learning more specifically. It is the next evolution
of machine learning. Let's take few
important parameter and compare machine learning
with deep learning. So starting with
data dependencies, the most important difference
between deep learning and machine learning is
its performance as the volume of the data gets
From the below graph. You can see that when the size of the data
is small deep learning algorithm doesn't perform that well, but why well, this is because deep
learning algorithm needs a large amount of data
to understand it perfectly on the other hand the machine
learning algorithm can easily work with smaller data set fine. Next comes the hardware
dependencies deep learning algorithms are heavily dependent
on high-end machines while the machine learning
algorithm can work on low and machines as Well, this is because the requirement of deep learning
algorithm include gpus which is an integral part of its working the Deep learning
algorithm requires gpus as they do a large amount of matrix
multiplication operations, and these operations can only be efficiently
optimized using a GPU as it is built for this purpose. Only our third parameter will be feature engineering well
feature engineering is a process of putting the domain knowledge
to reduce the complexity of the data. Make patterns more visible
to learning algorithms. This process is difficult
and expensive in terms of time and expertise in case
of machine learning most other features are needed
to be identified by an expert and then hand coded
as per the domain and the data type. For example, the features can be a pixel value shapes
texture position orientation or anything fine the performance of most of the machine
learning algorithm depends on how accurately the features
are identified and stood where as in case of deep learning algorithms it
try to learn high level features from the data. This is a very distinctive part
of deep learning which makes it way ahead of traditional machine learning
deep learning reduces the task of developing new feature
extractor for every problem like in the case of CNN algorithm it first try
to learn the low-level features of the image such as
edges and lines and then it proceeds
to the parts of faces of people and then finally to
the high-level representation of the face. I hope that things
Getting clearer to you. So let's move on ahead and see
the next parameter. So our next parameter is
problem solving approach when we are solving a problem
using traditional machine learning algorithm. It is generally recommended that we first break
down the problem into different sub parts
solve them individually and then finally combine them
to get the desired result. This is how the machine learning
algorithm handles the problem on the other hand
the Deep learning algorithm solves the problem
from end to end. Let's take an example. To understand this
suppose you have a task of multiple object detection. And your task is to identify. What is the object and where it
is present in the image. So, let's see and compare. How will you tackle
this issue using the concept of machine learning and deep learning starting
with machine learning in a typical machine
learning approach. You would first divide
the problem into two step first object detection
and then object recognization. First of all, you would use a bounding
box detection algorithm like grab could fight. Sample to scan through the image and find out all
the possible objects. Now, once the objects
are recognized you would use object recognization algorithm, like svm with hog
to recognize relevant objects. Now, finally, when you combine the result you
would be able to identify. What is the object
and where it is present in the image on the other hand
in deep learning approach. You would do the process
from end to end for example in a yellow net which is a type of deep learning
algorithm you would pass. An image and it would give out
the location along with the name of the object. Now, let's move on to
our fifth comparison parameter its execution time. Usually a deep learning
algorithm takes a long time to train this is because there's so many parameter in
a deep learning algorithm that makes the training longer than usual the training
might even last for two weeks or more than that. If you are training
completely from the scratch, whereas in the case
of machine learning, it relatively takes much
less time to train ranging from a few weeks. Too few Arts. Now. The execution time
is completely reversed when it comes to the testing
of data during testing the Deep learning algorithm
takes much less time to run. Whereas if you compare it
with a KNN algorithm, which is a type of machine
learning algorithm the test time increases as the size
of the data increase last but not the least we
have interpretability as a factor for comparison
of machine learning and deep learning. This fact is the main reason
why deep learning is still thought ten times
before anyone knew. Uses it in the industry. Let's take an example suppose. We use deep learning to give automated scoring two essays
the performance it gives and scoring is quite
excellent and is near to the human performance, but there's an issue with it. It does not reveal white has given that score
indeed mathematically. It is possible to find out that which node of a deep
neural network were activated, but we don't know what the neurons
are supposed to model and what these layers of neurons
are doing collectively. So if To interpret the result on the other hand machine
learning algorithm, like decision tree gives us
a crisp rule for void chose and watered chose. So it is particularly easy
to interpret the reasoning behind therefore the algorithms
like decision tree and linear or logistic regression are primarily used in
industry for interpretability. Let me summarize things for you machine learning
uses algorithm to parse the data learn from the data and make informed decision based
on what it has learned fine. in this deep learning structures
algorithms in layers to create artificial neural network that can learn and make Intelligent Decisions
on their own finally deep learning is a subfield
of machine learning while both fall
under the broad category of artificial intelligence
deep learning is usually what's behind the most human-like artificial
intelligence now in early days scientists
used to have a lab notebook to Test progress results and conclusions now
Jupiter is a modern-day to that allows data scientists to record the complete
analysis process much in the same way other scientists
use a lab notebook. Now, the Jupiter product was
originally developed as a part of IPython project the iPad and project was used to provide
interactive online access to python over time. It became useful to interact with other data analysis tools
such as are in the same manner with the split from python the tool crew in in his current
manifestation of Jupiter. Now IPython is
still an active tool that's available for use. The name Jupiter itself is
derived from the combination of Julia Python. And our while Jupiter runs code in many programming languages
python is a requirement for installing the jupyter
notebook itself now to download jupyter notebook. There are a few ways
in their official website. It is strongly recommended
installing Python and Jupiter using Anaconda distribution, which includes python
Don't know what book and other commonly used packages for scientific Computing
as well as data science. Although one can also do so using the pipe
installation method personally. What I would suggest
is downloading an app on a navigator, which is a desktop graphical user
interface included in Anaconda. Now, this allows you
to launch application and easily manage
conda packages environments and channels without the need
to use command line commands. So all you need to do is go
to another Corner dot orgy and inside you go. To Anaconda Navigators. So as you can see here, we have the conda installation
code which you're going to use to install it
in your particular PC. So either you can
use these installers. So once you download
the Anaconda Navigator, it looks something like this. So as you can see here, we have Jupiter lab jupyter
notebook you have QT console, which is IPython console. We have spider which is
somewhat similar to a studio in terms of python again, we have a studio
so we have orange three We have glue is
and we have VSC code. Our Focus today would be
on this jupyter notebook itself. Now when you
launch the Navigator, you can see there are
many options available for launching python as well. As our instances Now
by definition are jupyter. Notebook is fundamentally a Json file with
a number of annotations. Now, it has three main parts which are the metadata
The Notebook format and the list of cells now you should get yourself acquainted
with the environment that Jupiter user interface
has a number of components. So it's important to know what our components
you should be using on a daily basis and you
should get acquainted with it. So as you can see here our Focus today will be
on the jupyter notebook. So let me just launched
the Japan and notebook. Now what it does is creates
a online python instance for you to use it over the web. So let's launch now as you can see we have
Jupiter on the top left as expected and this acts
as a button to go to your home page
whenever you click on this you get back
to your particular home paste. Is the dashboard now there are
three tabs displayed with other files
running and clusters. Now, what will do is
will understand all of these three and understand what are the importance of these three tabs
other file tab shows the list of the current files
in the directory. So as you can see we have
so many files here. Now the running tab presents another screen of
the currently running processes and the notebooks now the
drop-down list for the terminals and notebooks are
populated with there. Running numbers. So as you can see inside, we do not have
any running terminals or there no running
notebooks as of now and the cluster tab presents another screen
to display the list of clusters available see in the
top right corner of the screen. There are three buttons
which are upload new and the refresh button. Let me go back
so you can see here. We have the upload new
and the refresh button. Now the upload button
is used to add files to The Notebook space and you
may also just drag and drop as you would
when handling files. Similarly, you can drag and drop notebooks
into specific folders as well. Now the menu with the new
in the top residents of further many
of text file folders terminal and Python 3. Now, the test file option
is used to add a text file to the current directory Jupiter
will open a new browser window for you for the running
new text editor. Now, the text entered
is automatically saved and will be displayed
in your notebooks files display. Now the folder option what it does is
creates a new folder. With the name Untitled folder
and remember all the files and folder names are editable. Now the terminal option
allows you to start and IPython session. The node would options
available will be activated when additional note books are
available in your environment. The Python 3 option is used
to begin pythons recession interactively in your note. The interface looks
like the following screen shot. Now what you have is
full file editing capabilities for your script
including saving as new file. You also have a complete ID for your python script now we
come to the refresh button. The refresh button is used
to update the display. It's not really necessary
as a display is reactive to any changes in
the underlying file structure. I had a talk with
the files tab item. There is a check box drop down menu and a home button
as you can see here. We have the checkbox
the drop-down menu and the home button. Now the check box is used
to toggle all the checkboxes in the item list. So as you can see you can select
all of these when either move or either delete all
of the file selected, It or what you
can do is select all and deselect some of the files as your wish now the drop
down menu presents a list of choices available, which are the folders
all notebooks running and files to the folder section will select all the folders
in the display and present account
of the folders in the small box. So as you can see here, we have 18 number of folders
now all the notebooks section will change the count
of the number of nodes and provide you
with three option so you can see here. It has selected all
the given notebooks which are a In a number and you get the option to either
duplicate the current notebook. You need to move it view
it edit it or delete. Now, the writing section
will select any running scripts as you can see here. We have zero running scripts and update the count
to the number selected. Now the file section
will select all the files in the notebook display and
update the counts accordingly. So if you select the files here, we are seven files
as you can see here. We have seven files
some datasets CSV files and text files now
the home button. Brings you back to the home
screen of the notebook. So on you to do is click
on the jupyter. Notebook lower. It will bring you back to
the Jupiter notebook dashboard. Now, as you can see
on the left hand side of every item is a checkbox
and I can and the items name. The checkbox is used to build
a set of files to operate upon and the icon is indicated
of of the type of the item. And in this case, all of the items are
folder here coming down. We have the ring notebooks. And finally we have certain
files which are the text files and the As we files
now a typical workflow of any jupyter. Notebook is to first
of all create a notebook for the project
or your data analysis. Add your analysis step coding and output and Surround
your analysis with organization and presentation mark
down to communicate and entire story
now interactive notebooks that include widgets
and display modules will then be used by others
by modifying parameters and the data to note the effects
of the changes now if we talk about security
jupyter notebooks are created in order to Be shared
with other users in many cases over the Internet. However, jupyter notebook
can execute arbitrary code and generate arbitrary code. This can be a problem. If malicious aspects
have been placed in the note Now the default
security mechanism for Japan or notebooks include raw HTML, which is always sanitized
and check for malicious coding. Another aspect is you cannot run
external Java scripts. Now the cell contents, especially the HTML and
the JavaScript are not trusted it requires user value. Nation to continue and the output from any cell
is not trusted all other HTML or JavaScript is never trusted and clearing the output
will cause the notebook to become trusted when save now notebooks
can also use a security digest to ensure the correct user
is modifying the contents. So for that what you
need to do is a digest what it does is takes
into the account the entire contents
of the notebook and a secret which is only known by
The Notebook Creator and this combination ensures that malicious coding is
is not going to be added to the notebook so you can add security
to address to notebook using the following command
which I have given here. So it's Jupiter the profile what you have selected
and inside you what you need to do is security
and notebook secret. So what you can do is
replace the notebooks secret with your putter secret and that will act as a key
for the particular notebook. So what you need to do
is share that particular key with all your colleagues or whoever you want to share
that particular notebook with and in that case, it keeps the notebooks. Geode and away from
other malicious coders and all other aspect
of Jupiter is configuration. So you can configure some
of the display parameters used and presenting notebooks. Now, these aren't configurable
due to the use of product known as code mirror to present
and modify the notebook. So cold mirror water basically
is it is a JavaScript based editor for the u.s. Within the web pages
and notebooks. So what you do is
what you do code mirror, so as you can see here code mirror is a versatile
text editor implemented. In JavaScript for the browser. So what it does is allow you to configure
the options for Jupiter. So now let's execute
some python code and understand the notebook
in A Better Way Jupiter does not interact with your scripts as
much as it executes your script and request the result. So I think this is
how jupyter notebooks have been extended to other
languages besides python as it just takes a script runs it against
a particular language engine and across the output
from the engine all the while not Really
knowing what kind of a script is being executed
now the new windows shows and empty cell for you to enter
the python code know what you need to do is under new
you select the Python 3 and what I will do is open
a new notebook. Now this notebook is Untitled. So let's give the new work area
and name python code. So as you can see we have
renamed this particular cell now order save option should be
on the next to the title as you can see last. Checkpoint a few days
ago, unsaved changes. The autosave option is
always on what we do is with an accurate name. We can find the selection and this particular
notebook very easily from The Notebook home page. So if you select
your browser's Home tab and refresh you will find this new window name
displayed here again. So if you just go
to a notebook home and as you can see, I mentioned it by then quotes
and under running. Also, you have the pilot
and quotes here. So let's get back
to the Particular page or the notebook
one thing to note here that it has and does an item icon
versus a folder icon though automatically
assigned extension as you can see here is ipy
and be the IPython note and says the item is in a browser
in a Jupiter environment. It is marked as running answer is a file by that name
in this directory as well. So if you go
to your directory, let me go and check it. So as you can see
if you go into the users are you can see we have the
in class projects that Python codes
like the series automatically have that particular
IPython notebook created in our working environment and the local disk space also. So if you open the IP y +
B file in a text editor, you will see basic context
of a Jupiter code as you can see if I'm opening it. The cells are empty. Nothing is there so let's type
in some code here. For example, I'm going to put
in name equals edgy Rekha. Next what I'm going to do
is provide subscribers that equals seven hundred gay
and to run this particular cell. What you need to do
is click on the run Icon and it will see
here we have one. So this is the first set to be
executed in the second cell. We enter python code that references the variables
from the first cell. So as you can see here, we have friend named
has strings subscribers. So let me just
run this particular. So as you can see here note. Now that we have an output here that Erica has 700k
YouTube subscriber now since more than 700 K now
to know more about Jupiter and other Technologies, what you can do is subscribe
to our Channel and get updates on the latest
trending Technologies. So note that Jupiter color codes your python just as
decent editor vote and we have empty braces to the left of each code block
such as you can see here. If we execute the cell the results are displayed
in line now, it's interesting that Jupiter keeps. The output last generated
in the saved version of the file and it's a save checkpoints. Now, if we were to rerun
your cells using the rerun or the run all the output
would be generated and c8y autosave now, the cell number is incremented
and as you can see if I rerun this you see
the cell number change from one to three and if I rerun this the Selma
will change from 2 to 4. So what Jupiter does is keeps
a track of the latest version of each cell so similarly if you are to close
the browser tab It's the display in the Home tab. You will find
a new item we created which is the python code
your notebook saved autosaved as you can see here
in the bracket has autosaved. So if we close this
in the home button, you can see here. We have python codes. So as you can see if we click
that it opens the same notebook. It has the previously displayed items will be always
there showing the output sweat that we generated
in the last run now that we have seen
how python Works in Jupiter including
the underlying encoding then how this python. This allows data set
or data set Works in Jupiter. So let me create
another new python notebook. So what I'm going to do
is name this as pandas. So from here, what we will do is read
in last dataset and compute some standard
statistics of data. Now what we are interested
in in seeing how to use the pandas in Jupiter how well the script performs and what information
is stored in the metadata, especially if it's
a large dataset so our Python script accesses
the iris dataset here that's built into one
of the Python packages. Now. All we are looking in to do is
to read in slightly large number of items and calculate
some basic operations on the data set. So first of all, what we need to do is from sklearn import
the data set so sklearn is scikit-learn and it is
another library of python. It contains a lot of data sets
for machine learning and all the algorithms which are present
for machine learning and the data sets
which are there so, So import was successful. So what we're going to do
now is pull in the IRS data. What we're going to do is Iris
underscore data set equals and the load on the screen now
that should do and I'm sorry, it's data set start lower. So so as you can see here, the number here
is considered three now because in the second drawer and we encountered
an error it was data set. He's not data set. So so what we're going to do is grab the first
two corner of the data. So let's pretend x equals. If you press the tab,
it automatically detects what you're going to write
as Todd datasets dot data. And what we're going to do is
take the first two rows comma not to run it
from your keyboard. All you need to do is
press shift + enter. So next what we're going
to do is calculate some basic statistics. So what we're going
to do is X underscore. Count equals x I'm going to use
the length function and said that we're going to use x
dot flat similarly. We going to see X-Men and X Max and the Min
our display our results. What we're going to do is you
just play the results now, so as you can see the counter 300
the minimum value is 3.8 m/s. And what is 0.4 and the mean is five point
eight four three three three. So let me connect you
to the real life and tell you what
all are the things which you can easily do using
the concepts of machine learning so you can easily get
answer to the questions like which types of house
lies in this segment or what is the market value
of this house or is this a male as spam or not spam? Is there any fraud? Well, these are some
of the question you could ask to the machine but for getting an answer
to these you need some algorithm the machine need to train
on the basis of some algorithm. Okay, but how will you
decide which algorithm to choose and when? Okay. So the best option for us is
to explore them one by one. So the first is
classification algorithm where the categories
predicted using the data if you have some question, like is this person a male or a female or is
this male a Spam or not? Spam then these category
of question would fall under the classification
algorithm classification is a supervised learning approach in which the computer program
learns from the input given to it and then uses this learning to classify
new observation some examples of classification problems are speech organization
handwriting recognized. Shouldn't biometric
identification document classification Etc. So next is the anomaly
detection algorithm where you identify
the unusual data point. So what is an anomaly detection. Well, it's a technique that is used to
identify unusual pattern that does not conform
to expected Behavior or you can say the outliers. It has many application in business like
intrusion detection, like identifying strange
patterns in the network traffic that could signal a hack
or system Health monitoring that is sporting a deadly tumor
in the MRI scan or you can even use it for fraud detection
credit card transaction or to deal with fault detection
in operating environment. So next comes
the clustering algorithm, you can use this clustering
algorithm to group the data based on some similar condition. Now you can get answer
to which type of houses lies in this segment or what type
of customer buys this product. The clustering is a task
of dividing the population or data points into
number of groups such that the data point
and the same groups are more. Hello to other data points
in the same group than those in the other groups
in simple words. The aim is to segregate
groups with similar trait and assigning them into cluster. Now this clustering is a task
of dividing the population or data points into
a number of groups such that the data points in the X group is more similar
to the other data points in the same group rather than
those in the other group. In other words. The aim is to segregate
the groups with similar traits and assigning them
into different clusters. Let's understand this with an example Suppose you are
the head of a rental store and you wish to understand
the preference of your customer to scale up your business. So is it possible for you to look at the detail
of each customer and design a unique business strategy
for each of them? Definitely not right? But what you can do is to
Cluster all your customer saying to 10 different groups based
on their purchasing habit and you can use
a separate strategy for customers in each
of these ten different groups. And this is
what we call clustering. Next we have regression
algorithm where the data itself is predicted question. You may ask to this type
of model is like what is the market value of this house or is it going to rain
tomorrow or not? So regression is one of the most
important and broadly used machine learning
and statistics tool. It allows you to make prediction
from data by learning the relationship between
the features of your data and some observe continuous
valued response regulation is used in a massive
number of application. You know, what stock Isis
prediction can be done using regression now, you know about different
machine learning algorithm. How will you decide
which algorithm to choose and when so let's cover
this part using a demo. So in this demo part what we will do will create six
different machine learning model and pick the best model
and build the confidence such that it has the most
reliable accuracy. So far our demo part
will be using the IRS data set. This data set is
quite very famous and is considered one of
the best small project to start with you can consider this as a hello world data set
for machine learning. So this data set consists of 150 observation
of Iris flower. Therefore Columns of measurement
of flowers in centimeters the fifth column
being the species of the flower observe all
the observed flowers belong to one of the three species
of Iris setosa Iris virginica and Iris versicolor. Well, this is
a good good project because it is so
well to understand the attributes are numeric. So you have to figure out
how to load and handle the data. It is a classification problem. Thereby allowing you to practice with perhaps an easier type of
supervised learning algorithm. It has only four
attributes and 150 rose. Meaning it is very small and can easily fit
into the memory and even all of the numeric attributes
are in same unit and the same scale means you do
not require any special scaling or transformation
to get started. So let's start coding and
as I told earlier for the But I'll be using Anaconda
with python 3.0 install on it. So when you install Anaconda how your Navigator
would look like. So there's my home page of
my anaconda navigator on this. I'll be using
the jupyter notebook, which is a web-based interactive
Computing notebook environment, which will help me to write and
execute my python codes on it. So let's hit the launch
button and execute our jupyter notebook. So as you can see that my jupyter notebook
is starting on localhost double eight nine zero. Okay, so there's
my jupyter notebook what I'll do here. I'll select new. book Python 3 Does
my environment where I can write and execute all
my python codes on it? So let's start
by checking the version of the libraries in order
to make this video short and more interactive
and more informative. I've already written
the set of code. So let me just copy
and paste it down. I'll explain you
then one by one. So let's start by checking the version
of the Python libraries. Okay, so there is
the code let's just copy it copied and let's paste it. Okay first let me summarize things for you
what we are doing here. We are just checking the version of the different
libraries starting with python will first
check what version of python we are working
on then we'll check what are the version
of sci-fi we are using the numpy matplotlib then
Panda then scikit-learn. Okay. So let's execute
the Run button and see what are the various
versions of libraries which we are using it the run. So we are working on Python 3
point 6 point 4 PSI by 1.0 now. By 1.1 for matplotlib 2.12
pandas 0.22 and scikit-learn or version 0.19. Okay. So these are the version which I'm using ideally your
version should be more recent or it should match
but don't worry if you lack
a few versions behind as the API is do not change
so quickly everything in this tutorial will very
likely still work for you. Okay, but in case you are getting an error stop
and try to fix that error in case you are unable to find
the solution for the error, feel free to reach out at Eureka
even after the This class. Let me tell you this if you are not able to run
the script properly, you will not be able
to complete this tutorial. Okay, so whenever you
get a doubt reach out to a deal-breaker
and just resolve it now, everything is working
smoothly then now is the time to load the data set. So as I said, I'll be using the iris flower
data set for this tutorial but before loading the data set, let's import all the modules
function and the object which we are going to use
in this tutorial same I've already written
the set of code. So let's just copy
and paste them. Let's load all the libraries. So these are
the various libraries which will be using
in our tutorial. So everything should work
fine without an error. If you get an error just
stop you need to work on your cyber environment
before you continue any further. So I guess everything
should work fine. Let's hit the Run
button and see. Okay, it worked. So let's now move ahead
and load the data. We can load the data direct from the UCI machine
learning repository. First of all, let me tell you we are using
Panda to load the data. Okay. So let's say my URL. Is this so This is My URL for the use
your machine learning repository from where I will be
downloading the data set. Okay. Now what I'll do, I'll specify the name
of each column when loading the data. This will help me later
to explore the data. Okay, so I'll just copy
and paste it down. Okay, so I'm defining
a variable names which consists of
various parameters including sepal length sepal
width petal length battle with and class. So these are just the name
of column from the data set. Okay. Now let's define the data set. So data set equals Panda
dot read underscore CSV inside that we are defining
URL and the names that is equal to name. As I already said we'll be using
Panda to load the data. Alright, so we are using
Panda dot read CSV, so we are reading. The CSV file and inside that from where that CSV is coming
from the URL which you are. So there's my URL. Okay name sequel names. It's just specifying the names
of the various columns in that particular CSV file. Okay. So let's move forward
and execute it. So even our data set is loaded. In case you have some network
issues just go ahead and download the iris data file
into your working directory and loaded using the same method
but your make sure that you change the url
to the local name or else you might get an error. Okay. Yeah, our data set is loaded. So let's move ahead
and check out data set. Let's see how many columns
or rows we have in our data set. Okay. So let's print the number of rows and columns
in our data set. So our data set is
data set dot shape what this will do. It will just give you
the numbers of total number of rows and 2. Little more of column
or you can say the total number of instances are attributes
in your data set fine. So print data set dot shape
audio getting 150 and 500. So 150 is the total number
of rows in your data set and five is the total number
of columns fine. So moving on ahead. What if I want to see
the sample data set? Okay. So let me just print
the first certain instances of the data set. Okay, so print data set. Head. What I want is the first
30 instances fine. This will give me the first
30 result of my data set. Okay. So when I hit the Run button what I am getting is
the first 30 result, okay 0 to 29. So this is how my sample data set looks
like sepal length sepal width petal and petal width
and the class, okay. So this is how our data
set looks like now, let's move on and look at the summary
of each attribute. What if I want to find out
the count mean the minimum and the maximum values and
some other percentiles as well. So what should I do then for that print data
set dot described. What did we give let's see. So you can see that all the numbers are
the same scales of similar range between 0 to 8 centimeters, right the mean value
the standard deviation the minimum value
the 25 percentile 50 percentile 75 percentile the maximum value all
these values lies in the range between
0 to 8 centimeter. Okay. So what we just did is
we just took a summary of each attribute. Now, let's look
at the number of instances that belong to each class. So for that what we'll do
print data set. First of all, so let's print data set and I want to group it
Group by using class and I want the size
of it size of each class fine, and let's hit the Run. Okay. So what I want to do, I want to print
print out data set. However want to get it. I want it by class. So Group by class. Okay. Now I want the size of each class find
the size of each class. So Group by class dot size
and skewed the run so you can see that I have 50 instances
of Iris setosa 50 instances of Iris versicolor and 50 instances
of Iris virginica. Okay, all our of data type
integer of base64 fine. So now we have a basic
idea of Data, now, let's move ahead and create
some visualization for it. So for this we are going
to create two different types of plot first would be
the univariate plot and the next would be
the multivariate plot. So we'll be creating univariate
plots to better understand about each attribute and the next will be creating
the multivariate plot to better understand the relationship
between different attributes. Okay. So we start with
some univariate plot that is plot
of each individual variable. So given that the input
variables are numeric we can create box
and whiskers plot for it. Okay. So let's move ahead and create
a box and whiskers plot so data set Dot Plot. What kind I want it's a box. Okay, I'm do I need a subplot? Yeah, I need subplots for that. So subplots equal to what type of layout do I won't so
my layout structure is 2 cross 2 next do I want
to share my coordinates X and Y coordinates. No, I don't want to share it. So share x equal false and even share why
that 2 equals false? Okay. So we have our data set
Dot Plot kind equal box. My subplots is to lay out
to Us too and then what I want to do it, I want to see so Plot show
whatever I created short. Okay, execute it. Not just gives us
a much clearer idea about the distribution
of the input attribute. Now what if I had given
the layout to 2 cross 2 instead of that I would have given
it for cross for so what it will result
just see fine. Everything would be printed
in just one single row. Hold on guys area is a doubt. He's asking that why
we're using the sheriff's and share y values. What are these why we have
assigned false values to it? Okay Ariel. So in order to
resolve this query, I need to show you
what will happen if I give True Values to them. Okay, so be with me
so share its go. Pull through and share why
that equals true. So let's see
what result will get. You're getting it the X and y-coordinates are just
shared among all the for visualization. Right? So are you can see that the sepal length
and sepal width has y values ranging from zero point
zero two seven point five which are being shared among both the visualization so
is with the petal length. It has shared value between zero point
zero two seven point five. Okay, so that is why
I don't want to share the value of X and Y, so it's just giving us
a cluttered visualization. So Aria why I'm doing this. I'm just doing it cause I don't want my X
and Y coordinates To be shared among any visualization. Okay. That is why my share X and share
by value are false. Okay, let's execute it. So this is a pretty
much Clear visualization which gives a clear idea
about the distribution of the input attribute. Now if you want you
can also create a histogram of each input variable
to get a clear idea of the distribution. So let's create
a histogram for it. So data set dot his okay. I would need to see it. So plot dot show. Let's see. So there's my histogram
and it seems that we have two input variables
that have a go. And distribution so
this is useful to note as we can use the algorithms that can exploit
this assumption. Okay. So next comes
the multivariate lat now that we have created the
univariate plot to understand about each attribute. Let's move on and look
at the multivariate plot and see the interaction between
the different variables. So first, let's look
at the scatter plot of all the attribute
this can be helpful to spot structured relationship
between input variables. Okay. So let's create
a scatter Matrix. So for creating a scatter plot,
we need scatter Matrix, and we need to pass
our data set into It okay. And then what I want
I want to see it. So plot dot show. So this is
how my scatter Matrix looks like it's like that the diagonal grouping
of some pear, right? So this suggests
a high correlation and a predictable relationship. All right. This was our multivariate plot. Now, let's move on
and evaluate some algorithm that's time to create
some model of the data and estimate the accuracy
on the basis of unseen data. Okay. So now we know all
about our data set, right? We know how many instances and attributes are
there in our data set. We know the summary
of each attribute. So I guess we have seen much
about our data set. Now. Let's move on
and create some algorithm and estimate their accuracy
based on the Unseen data. Okay. Now what we'll do we'll create
some model of the data and estimate the accuracy based
on the some unseen data. Okay. So for that first of all, let's create a
validation data set. What is the validation data
set validation data set is your training data set that will be using it
to trainer model fine. All right. So how will create
a validation data set for creating
a validation data set? What we are going to do is we
are going to split our data set into two point. Okay. So the very first thing
we'll do is to create a validation data set. So why do we even need
a validation data set? So we need a validation
data set know that the model we
created is any good later. What we'll do we'll use
the statistical method to estimate the accuracy
of the model that we create on the Unseen data. We also want a more concrete
estimate of the accuracy of the best model on unseen data by evaluating it
on the actual unseen data. Okay confused. Let me simplify this for you. What we'll do we'll
split the loaded data into two parts the first
80 percent of the data. User to train our model
and the rest 20% will hold back as the validation data set that will use it to verify
our trained model. Okay fine. So let's define an array. This is my ra water it
will consist of will consist of all the values
from the data set. So data set dot values. Okay next. I'll Define a variable X which will consist
of all the column from the array from 0
to 4 starting from 0 to 4 and the next variable Y which would consist of
of the array starting from this. So first of all, we will Define a variable X
that will consist of the values in the array starting
from the beginning 0 Del for okay. So these are the column
which will include in the X variable and for a y variable I'll Define
it as a class or the output. So what I need, I just need the fourth column
that is my class column. So I'll start it
from the beginning and I just want
the fourth column. Okay now I'll Define
the my validation size. Validation underscore sighs, I'll Define it as 0.20
and our use a seed I Define CD equals 6. So this method seed sets
the integers starting value used in generating random number. Okay, I'll Define the value
of C R equals x. I'll tell you what is
the importance of that later on? Okay. So let me Define first
few variables such as X underscore train test
why underscore train and why underscore test Okay, so What do you want to do
is Select some model. Okay, so module
underscore selection. But before doing that what we have to do
is split our training data set into two halves. Okay, so dot train underscore
test underscore split what you want to split
is a value of X and Y. Okay and my test size is equals
to validation size, which is a 0.20 correct
and my random state. Is equal to seed
so what the city is doing? It's helping me to keep the same
Randomness in the training and testing data set fine. So let's execute it and see
what is our result. It's executed next. We'll create a test
harness for this. We'll use 10-fold
cross-validation to estimate the accuracy. So what it will do it
will split a data set into 10 parts crane
on the nine part and test on the one part and this will repeat
for all combination of train and test pilots. Okay. So for that, let's define again my CD
that was six already Define and scoring
equals accuracy fine. So we are using the metric of
accuracy to evaluate the model. So what is this? This is a ratio of number
of correctly predicted instances divided by the total number
of instances in the data set x hundred giving a
percentage example. It's 98% accurate or 99%
accurate things like that. Okay, so we'll be
In the scoring variable when we run the build and evaluate each model
in the next step. The next part is building
model till now. We don't know which algorithm
would be good for this problem or what configuration to use. So let's begin with
six different algorithm. I'll be using logistic regression linear
discriminant analysis, k-nearest neighbor
classification and regression trees neighbor buys. And what Vector machine well these algorithms chime using is
a good mixture of simple linear or non-linear algorithms
in simple linear switch. Included the logistic regression and the linear discriminant
analysis or the nonlinear part which included the KNN
algorithm the card algorithm that the neighbor buys
and the support Vector machines. Okay. So we reset
the random number seed before each run
to ensure that evaluation of each algorithm is performed using exactly
the same data spreads. It ensures the result
are directly comparable. Okay, so, let me
just copy and paste it. Okay. So what we're doing here, we are building
five different types of model. We are building
logistic regression linear discriminant analysis, k-nearest neighbor decision
tree ghajini buys and the support Vector machine. Okay next what we'll do we'll
evaluate model in each turn. Okay. So what is this? So we have six different model and accuracy estimation
for each one of them now we need to compare
the model to each other and select the most
accurate of them all. So running the script we
saw the following result so we can see some
of the results on the screen. What is It is just the accuracy score using
different set of algorithms. Okay, when we are using
logistic regression, what is the accuracy rate when we are using
linear discriminant algorithm? What is the accuracy
and so-and-so? Okay. So from the output with seems that LD algorithm was
the most accurate model that we tested now, we want to get an idea
of the accuracy of the model on our validation set
or the testing data set. So this will give us
an independent final check on the accuracy
of the best model. It is always valuable
to keep our testing data set for just in case you
made a our overfitting to the testing data set or you made a data leak
both will result in an overly optimistic result. Okay, you can run the ldo model
directly on the validation set and summarize the result as
a final score a confusion Matrix and a classification statistics
and probability are essential because these disciples
form the basic Foundation of all machine learning
algorithms deep learning. Social intelligence
and data science, in fact mathematics
and probability is behind everything around us
from shapes patterns and colors to the count of petals in a flower
mathematics is embedded in each and every
aspect of our lives. So I'm going to go ahead
and discuss the agenda for today with you
all we're going to begin the session by understanding what is data after that. We'll move on and look
at the different categories of data like quantitative
and Qualitative data, then we'll discuss what
exactly statistics is the basic terminologies in
statistics and a couple of sampling techniques. Once we're done with that. We'll discuss a different
types of Statistics which involve descriptive
and inferential statistics. Then in the next session, we will mainly be focusing
on descriptive statistics here will understand
the different measures of center measures
of spread Information Gain and entropy will also understand all of these measures
with the help of a user. And finally, we'll discuss what
exactly a confusion Matrix is. Once we've covered the entire descriptive
statistics module will discuss the probability module here will understand
what exactly probability is the different
terminologies in probability. We will also study the different
probability distributions, then we'll discuss the types
of probability which include marginal probability joint
and conditional probability. Then we move on and discuss a use case
wherein we will see examples that show us how the different types
of probability work and to better
understand Bayes theorem. We look at a small example. Also, I forgot to mention that at the end of the
descriptive statistics module will be running a small demo
in the our language. So for those of you
who don't know much about our I'll be explaining
every line in depth, but if you want to have
a more in-depth understanding about our I'll leave
a couple of blocks. And a couple of videos
in the description box you all can definitely
check out that content. Now after we've completed the
probability module will discuss the inferential statistics
module will start this module by understanding what is point
estimation will discuss what is confidence interval
and how you can estimate the confidence interval will
also discuss margin of error and will understand all
of these concepts by looking at a small use case. We finally end the inferential
Real statistic module by looking at what hypothesis
testing is hypothesis. Testing is a very important part
of inferential statistics. So we'll end the session
by looking at a use case that discusses how
hypothesis testing works and to sum everything up. We'll look at a demo that explains how
inferential statistics works. Right? So guys, there's
a lot to cover today. So let's move ahead and take
a look at our first topic which is what is data. Now, this is
a quite simple question if I ask any of You
what is data? You'll see that it's
a set of numbers or some sort of documents that have stored in my computer
now data is actually everything. All right, look around you there
is data everywhere each click on your phone generates
more data than you know, now this generated data
provides insights for analysis and helps us make
Better Business decisions. This is why data is
so important to give you a formal definition data refers
to facts and statistics. Collected together
for reference or analysis. All right. This is the definition
of data in terms of statistics and probability. So as we know data
can be collected it can be measured and analyzed it can be visualized by
using statistical models and graphs now data is divided
into two major subcategories. Alright, so first we
have qualitative data and quantitative data. These are the two
different types of data under qualitative data. I'll be have nominal
and ordinal data and under quantitative data. We have discrete
and continuous data. Now, let's focus
on qualitative data. Now this type of data deals with
characteristics and descriptors that can't be easily measured but can be observed subjectively now qualitative data
is further divided into nominal and ordinal data. So nominal data is
any sort of data that doesn't have
any order or ranking? Okay. An example of nominal
data is gender. Now. There is no ranking in gender. There's only male female
or other right? There is no one two, three four or any sort
of ordering in gender race is another example of nominal data. Now ordinal data is basically an
ordered series of information. Okay, let's say
that you went to a restaurant. Okay. Your information is stored
in the form of customer ID. All right. So basically you are represented
with a customer ID. Now you would have rated
their service as either good or average. All right, that's
how no ordinal data is and similarly they'll have
a record of other customers who visit the restaurant
along with their ratings. All right. So any data which has
some sort of sequence or some sort of order
to it is known as ordinal data. All right, so guys, this is pretty simple
to understand now, let's move on and look
at quantitative data. So quantitative data
basically these He's with numbers and things. Okay, you can understand that by the word quantitative
itself quantitative is basically quantity. Right Saudis with numbers
a deals with anything that you can measure
objectively, right? So there are two types of quantitative data there is
discrete and continuous data now discrete data is also
known as categorical data and it can hold a finite number
of possible values. Now, the number of students
in a class is a finite Number. All right, you can't
have infinite number of students in a class. Let's say in your fifth grade. There were a hundred students
in your class. All right, there weren't
infinite number but there was a definite finite number
of students in your class. Okay, that's discrete data. Next. We have continuous data. Now this type of data
can hold infinite number of possible values. Okay. So when you say weight
of a person is an example of continuous data what I mean to see is
my weight can be 50 kgs or it Can be 50.1 kgs or it can be 50.00 one kgs
or 50.000 one or is 50.0 2 3 and so on right? There are infinite number
of possible values, right? So this is what I mean
by continuous data. All right. This is the difference between
discrete and continuous data. And also I would like to mention
a few other things over here. Now, there are a couple
of types of variables as well. We have a discrete variable and we have a continuous
variable discrete variable is also known as
a categorical variable or and it can hold values
of different categories. Let's say that you have
a variable called message and there are two types
of values that this variable can hold let's say that your message
can either be a Spam message or a non spam message. Okay, that's when you call
a variable as discrete or categorical variable. All right, because it
can hold values that represent different
categories of data now continuous variables
are basically variables that can store in
finite number of values. So the weight of a person
can be denoted as a continuous variable. All right, let's say there is
a variable called weight and it can store infinite number
of possible values. That's why we'll call it
a continuous variable. So guys basically
variable is anything that can store a value right? So if you associate any sort
of data with a A table, then it will become
either discrete variable or continuous variable. There is also dependent and
independent type of variables. Now, we won't discuss all
with that in depth because that's pretty understandable. I'm sure all of you know, what is independent variable
and dependent variable right? Dependent variable is
any variable whose value depends on any other
independent variable? So guys that much
knowledge I expect or if you do have all right. So now let's move on and look
at our next topic which Which is what is statistics now coming
to the formal definition of statistics statistics is
an area of Applied Mathematics, which is concerned with data collection
analysis interpretation and presentation now usually when I speak about statistics
people think statistics is all about analysis but statistics has other path
toward it has data collection is also part of Statistics data
interpretation presentation. All of this comes into statistics already are
going to use statistical methods to visualize data to collect
data to interpret data. Alright, so the area
of mathematics deals with understanding how data can be used
to solve complex problems. Okay. Now I'll give you
a couple of examples that can be solved
by using statistics. Okay, let's say that your company
has created a new drug that may cure cancer. How would you conduct
a test to confirm the As Effectiveness now, even though this sounds
like a biology problem. This can be
solved with Statistics. All right, you will have
to create a test which can confirm
the effectiveness of the drum or a this is a common problem that can be solved
using statistics. Let me give you
another example you and a friend are at a baseball
game and out of the blue. He offers you a bet that neither team will hit
a home run in that game. Should you take the BET? All right here you just
discuss the probability of I know you'll win or lose. All right, this
is another problem that comes under statistics. Let's look at another example. The latest sales data
has just come in and your boss wants
you to prepare a report for management on places where the company
could improve its business. What should you look for? And what should you
not look for now? This problem involves a lot of data analysis will have to
look at the different variables that are causing
your business to go down or the you have to look
at a few variables. That are increasing
the performance of your models and does growing your business. Alright, so this involves
a lot of data analysis and the basic idea behind data analysis is
to use statistical techniques in order to figure
out the relationship between different variables or different components
in your business. Okay. So now let's move on and look at our next topic which is basic
terminologies and statistics. Now before you dive deep
into statistics, it is important that you understand
the basic terminologies used in statistics. The two most important
terminologies in statistics are population and Sample. So throughout the statistics
course or throughout any problem that you're trying
to stall with Statistics. You will come
across these two words, which is population and Sample
Now population is a collection or a set of individuals
or objects or events. Events whose properties
are to be analyzed. Okay. So basically you can refer
to population as a subject that you're trying to analyze
now a sample is just like the word suggests. It's a subset of the population. So you have to make sure
that you choose the sample in such a way that it represents
the entire population. All right. It shouldn't Focus add one part
of the population instead. It should represent
the entire population. That's how your sample
should be chosen. So Well chosen sample
will contain most of the information about a
particular population parameter. Now, you must be wondering
how can one choose a sample that best represents
the entire population now sampling is a statistical method that deals with the selection
of individual observations within a population. So sampling is performed in order to infer statistical
knowledge about a population. All right, if you
want to understand the different statistics
of a population like the mean the median Median the mode
or the standard deviation or the variance of a population. Then you're going
to perform sampling. All right, because it's not reasonable for
you to study a large population and find out the mean median
and everything else. So why is sampling
performed you might ask? What is the point of sampling? We can just study
the entire population now guys, think of a scenario where in you're asked
to perform a survey about the eating habits
of teenagers in the US. So at present there are
over 42 million teens in the US and this number is growing as we are speaking
right now, correct. Is it possible to survey each
of these 42 million individuals about their health? Is it possible? Well, it might be possible but this will take
forever to do now. Obviously, it's not it's
not reasonable to go around knocking each door and asking for what does
your teenage son eat and all of that right? This is not very reasonable. That's Why sampling is used? It's a method wherein a sample
of the population is studied in order to draw inferences
about the entire population. So it's basically
a shortcut to starting the entire population instead
of taking the entire population and finding out
all the solutions. You just going to take
a part of the population that represents the
entire population and you're going to perform
all your statistical analysis your inferential statistics
on that small sample. All right, and that sample basically here
Presents the entire population. All right, so I'm sure
have made this clear to you all what is sample
and what is population now? There are two main types
of sampling techniques that are discussed today. We have probability sampling
and non-probability sampling now in this video will only be focusing on
probability sampling techniques because non-probability sampling
is not within the scope of this video. All right will only discuss
the probability part because we're focusing on Statistics and
probability correct. Now again under
probability sampling. We have three different types. We have random
sampling systematic and stratified sampling. All right, and just
to mention the different types of non-probability sampling zwi have
no ball Kota judgment and convenience sampling. All right now guys
in this session. I'll only be
focusing on probability. So let's move on and look at the different types
of probability sampling. So what is Probability sampling. It is a sampling technique in which samples
from a large population are chosen by using
the theory of probability. All right, so there
are three types of probability sampling. All right first we have
the random sampling now in this method each member of the population
has an equal chance of being selected in the sample. All right. So each and every individual
or each and every object in the population
has an equal chance of being a A part of the sample. That's what random
sampling is all about. Okay, you are randomly going
to select any individual or any object. So this Bay each individual has an equal chance
of being selected. Correct? Next. We have systematic sampling now in systematic sampling
every nth record is chosen from the population to be
a part of the sample. All right. Now refer this image that I've shown over here
out of these six groups every Skinned group
is chosen as a sample. Okay. So every second record
is chosen here and this is our systematic sampling works. Okay, you're randomly
selecting the nth record and you're going to add
that to your sample. Next. We have stratified sampling. Now in this type of technique a stratum
is used to form samples from a large population. So what is a stratum
a stratum is basically a subset of the population that shares at
least one comment. Characteristics so let's say that your population has a mix
of both male and female so you can create to straightens out of this one will have
only the male subset and the other will have
the female subset or a this is what stratum is
it is basically a subset of the population that shares at least
one common characteristics. All right in our example,
it is gender. So after you've created a stratum you're going
to use random sampling on the stratums and you're going
to choose a final Samba. But so random sampling meaning that all of the individuals in each of the stratum
will have an equal chance of being selected
in the sample, correct. So Guys, these were
the three different types of sampling techniques. Now, let's move on and look
at our next topic which is the different
types of Statistics. So after this, we'll be looking at the more
advanced concepts of Statistics, right so far we discuss
the basics of Statistics, which is basically what is statistics
the different sampling. Techniques and the
terminologies and statistics. All right. Now we look at the different
types of Statistics. So there are two major
types of Statistics descriptive statistics and inferential statistics
in today's session. We will be discussing
both of these types of Statistics in depth. All right, we'll also
be looking at a demo which I'll be running
in the our language in order to make
you understand what exactly descriptive and inferential
statistics is so guys, which is going to look
at the 600 don't worry, if you don't
have much knowledge, I'm explaining everything
from the basic level. All right, so guys descriptive
statistics is a method which is used to describe
and understand the features of specific data set by giving
a short summary of the data. Okay, so it is mainly focused upon the
characteristics of data. It also provides a graphical
summary of the data now in order to make you understand
what descriptive statistics is, let's suppose. Pose that you want to gift all
your classmates or t-shirt. So to study the average
shirt size of a student in a classroom. So if you were to use
descriptive statistics to study the average shirt size
of students in your classroom, then what you would do is you
would record the shirt size of all students in the class and then you would find out
the maximum minimum and average shirt size of the club. Okay. So coming to inferential
statistics inferential statistics makes Is and predictions about
a population based on the sample of data taken
from the population? Okay. So in simple words, it generalizes a large data set
and it applies probability to draw a conclusion. Okay. So it allows you
to infer data parameters based on a statistical model
by using sample data. So if we consider
the same example of finding the average shirt size
of students in a class in infinite shal statistics, you will take a sample. All set of the class which is basically a few people
from the entire class. All right, you already
have had grouped the class into large medium and small. All right in this method
you basically build a statistical model and expand it for the entire
population in the class. So guys, there was a brief
understanding of descriptive and inferential statistics. So that's the difference
between descriptive and inferential now
in the next section, we will go in depth
about descriptive statistics. All right, so, That's a discuss more
about descriptive statistics. So like I mentioned earlier descriptive
statistics is a method that is used to describe
and understand the features of a specific data set by giving
short summaries about the sample and measures of the data. There are two important measures
in descriptive statistics. We have measure
of central tendency, which is also known as measure of center and we have
measures of variability. This is also known as
measures of spread. Ed so measures of center
include mean median and mode now what is measures of center measures of the center
are statistical measures that represent the summary
of a data set? Okay, the three main measures
of center are mean median and mode coming
to measures of variability or measures of spread. We have range
interquartile range variance and standard deviation. All right. So now let's discuss each of these measures
in a little more. Up starting with
the measures of center. Now. I'm sure all of you know what
the mean is mean is basically the measure of the average
of all the values in a sample. Okay, so it's basically
the average of all the values in a sample. How do you measure the mean I
hope all of you know how the main is measured if there are 10 numbers and you want to find the mean
of these 10 numbers. All you have to do is you have
to add up all the 10 numbers and you have to divide
it by 10 then here represents the Number
of samples in your data set. All right, since we
have 10 numbers, we're going to
divide this by 10. All right, this will
give us the average or the mean so to better
understand the measures of central tendency. Let's look at an example. Now the data set over here is
basically the cars data set and it contains a few variables. All right, it has
something known as cars. It has mileage per gallon cylinder
type displacement horsepower and roll axle ratio. All right, all of these measures
are related to cars. Okay. So what you're going
to do is you're going to use descriptive analysis and you're going to analyze
each of the variables in the sample data set for the mean standard
deviation median mode and so on. So let's say that you want
to find out the mean or the average horsepower of the cars among
the population of cards. Like I mentioned earlier what you'll do is you will check
the average of all the values. So in this case, we will take the sum
of the horizontal. Horsepower of each car and we'll divide that
by the total number of cards. Okay, that's exactly what I've done here
in the calculation part. So this hundred
and ten basically represents the horsepower
for the first car. Alright, similarly. I've just added up all
the values of horsepower for each of the cars and I've divided it by 8 now
8 is basically the number of cars in our data set. All right, so hundred and three
point six two five is what our mean is or Average
of horsepower is all right. Now, let's understand
what median is with an example? Okay. So to Define median median
is basically a measure of the central value of the sample set
is called the median. All right, you can see
that it is a middle value. So if we want to find
out the center value of the mileage per gallon
among the population of cars first, what we'll do is we'll arrange
the MGP values in ascending or descending order and Choose a middle value
right in this case since we have
eight values, right? We have eight values
which is an even entry. So whenever you have even
number of data points or samples in your data set, then you're going
to take the average of the two middle values. If we had nine values over here. We can easily figure
out the middle value and you know choose
that as a median. But since they're even number
of values we're going to take the average
of the two middle values. All right, so, Eight and twenty three
are my two middle values and I'm taking
the mean of those 2 and hence I get
twenty two point nine, which is my median. All right. Lastly let's look at
how mode is calculated. So what is mode the value that is most recurrent in the sample set is known as
mode or basically the value that occurs most often. Okay, that is known as mode. So let's say that we want to find out
the most common type of cylinder among the population of cards all we have to Do
is we will check the value which is repeated
the most number of times here. We can see that the cylinders
come in two types. We have cylinder of Type
4 and cylinder of type 6, right? So take a look at the data set. You can see that the most
recurring value is 6 right. We have one two,
three four and five. We have five six
and we have one two, three. Yeah, we have three four types
of lenders and 5/6. Cylinders. So basically we have three four type cylinders and we
have five six type cylinders. All right. So our mode is going
to be 6 since 6 is more recurrent than 4 so guys those were the measures of the center or the measures
of central tendency. Now, let's move on and look
at the measures of the spread. All right. Now, what is the measure
of spread a measure of spread? Sometimes also called as measure of dispersion is used
to describe the The variability in a sample or population. Okay, you can think
of it as some sort of deviation in the sample. All right. So you measure this with the help of the different
measure of spreads. We have range
interquartile range variance and standard deviation. Now range is pretty
self-explanatory, right? It is the given measure of
how spread apart the values in a data set are
the range can be calculated as shown in this formula. So you're basically going
to The maximum value in your data set from the minimum value
in your data set. That's how you calculate
the range of the data. Alright, next we
have interquartile range. So before we discuss
interquartile range, let's understand. What a quartile is red. So quartiles basically tell us
about the spread of a data set by breaking the data set
into different quarters. Okay, just like how the median breaks
the data into two parts. The quartile will break it. In two different quarters, so to better understand
how quartile and interquartile are calculated. Let's look at a small example. Now this data set basically
represents the marks of hundred students
ordered from the lowest to the highest scores red. So the quartiles lie in the following ranges
the first quartile, which is also known as q1 it lies between the 25th
and 26th observation. All right. So if you look at this
I've highlighted the 25th and the Six observation. So how you can calculate
Q 1 or first quartile is by taking the average
of these two values. Alright, since both
the values are 45 when you add them up
and divide them by two you'll still get 45 now
the second quartile or Q 2 is between the 50th
and the fifty first observation. So you're going to take
the average of 58 and 59 and you will get
a value of 58.5 now, this is my second quarter
the third quartile Q3. Is between the 75th and the 76th observation here
again will take the average of the two values which is the 75th value
and the 76 value right and you'll get a value of 71. All right, so guys
this is exactly how you calculate
the different quarters. Now, let's look at
what is interquartile range. So IQR or the interquartile
range is a measure of variability based on dividing
a data set into quartiles. Now, the interquartile
range is Calculated by subtracting the q1 from Q3. So basically Q3 minus q1 is your IQ are so
your IQR is your Q3 minus q1? All right. Now this is how each of the quartiles are each core
tile represents a quarter, which is 25% All right. So guys, I hope all
of you are clear with the interquartile range
and what our quartiles now, let's look at
variance covariance is basically a measure
that shows how much a I'm variable the first
from its expected value. Okay. It's basically the variance
in any variable now variance can be calculated by using
this formula right here x basically represents
any data point in your data set n is the total number
of data points in your data set and X bar is basically
the mean of data points. All right. This is how you calculate
variance variance is basically a Computing
the squares of deviations. Okay. That's why it says
s Square there now. Look at what is deviation
deviation is just the difference between each element
from the mean. Okay, so it can be calculated
by using this simple formula where X I basically
represents a data point and mu is the mean
of the population or add this is exactly how you calculate the deviation
Now population variance and Sample variance
are very specific to whether you're calculating
the variance in your population data
set or in your sample data set now the only
difference between Elation and Sample variance. So the formula for population variance
is pretty explanatory. So X is basically
each data point mu is the mean of the population n is the number of samples
in your data set. All right. Now, let's look at sample. Variance Now sample variance is the average of squared
differences from the mean. All right here x
i is any data point or any sample in your data
set X bar is the mean of your sample. All right. It's not the main
of your population. It's the If your sample and if you notice any here is
a smaller n is the number of data points in your sample. And this is basically
the difference between sample and population variance. I hope that is clear coming to standard deviation is
the measure of dispersion of a set of data from its mean. All right, so it's basically
the deviation from your mean. That's what standard deviation
is now to better understand how the measures
of spread are calculated. Let's look at a small use case. So let's say the Daenerys
has 20 dragons. They have the numbers
nine to five four and so on as shown on the screen, what you have to do is
you have to work out the standard deviation or at in order to calculate
the standard deviation. You need to know the mean right? So first you're going to find
out the mean of your sample set. So how do you calculate
the mean you add all the numbers in your data set and divided by the total number
of samples in your data set so you get a value of 7 here then you I'll clear the rhs of
your standard deviation formula. All right, so from
each data point you're going to subtract the mean
and you're going to square that. All right. So when you do that, you will get
the following result. You'll basically get
this 425 for 925 and so on so finally you
will just find the mean of the squared differences. All right. So your standard deviation will come up to two point
nine eight three once you take the square root. So guys, this is pretty simple. It's a simple
mathematic technique. All you have to do is you have
to substitute the values in the formula. All right. I hope this was clear
to all of you. Now let's move on and discuss the next topic
which is Information Gain and entropy now. This is one of my favorite
topics in statistics. It's very interesting and
this topic is mainly involved in machine learning algorithms, like decision trees
and random forest. All right, it's very important for you to know how Information Gain and entropy
really work and why they are so essential in building
machine learning models. We focus on the statistic parts
of Information Gain and entropy and after that we'll discuss
As a use case and see how Information Gain and entropy is used
in decision trees. So for those of you who don't know what
a decision tree is it is basically a machine
learning algorithm. You don't have to know
anything about this. I'll explain
everything in depth. So don't worry. Now. Let's look at what exactly
entropy and Information Gain Is As entropy is
basically the measure of any sort of uncertainty
that is present in the data. All right, so it can be measured
by using this formula. So here s is the set
of all instances in the data set or although data items
in the data set n is the different type of classes in your data set
Pi is the event probability. Now this might seem
a little confusing to you all but when we
go to the use case, you'll understand all
of these terms even better. All right cam. To Information Gain as the word suggests
Information Gain indicates how much information
a particular feature or a particular variable gives
us about the final outcome. Okay, it can be measured
by using this formula. So again here hedge
of s is the entropy of the whole data set
s SJ is the number of instances with the J value of an attribute a s is
the total number of instances in the data set V is the set of distinct values
of an attribute a hedge of SJ is the entropy
of subsets of instances and hedge of a comma s is
the entropy of an attribute a even though
this seems confusing. I'll clear out the confusion. All right, let's discuss
a small problem statement where we will understand how Information Gain and entropy is used to study
the significance of a model. So like I said Information Gain and entropy are very
important statistical measures that let us understand the significance of
a predictive model. Okay to get a more
clear understanding. Let's look at a use case. All right now suppose we
are given a problem statement. All right, the statement is
that you have to predict whether a match can be played or Not by studying
the weather conditions. So the predictor variables here
are outlook humidity wind day is also a predictor variable. The target variable
is basically played already. The target variable
is the variable that you're trying to protect. Okay. Now the value of the target
variable will decide whether or not a game
can be played. All right, so that's
why The play has two values. It has no and yes, no, meaning that the weather
conditions are not good. And therefore you
cannot play the game. Yes, meaning that the weather
conditions are good and suitable for you to play the game. Alright, so that was
a problem statement. I hope the problem statement
is clear to all of you now to solve such a problem. We make use of something
known as decision trees. So guys think
of an inverted tree and each branch of the tree
denotes some decision. All right, each branch is
Is known as the branch node and at each branch node, you're going to take
a decision in such a manner that you will get an outcome
at the end of the branch. All right. Now this figure
here basically shows that out of 14 observations
9 observations result in a yes, meaning that out of 14 days. The match can be played
only on nine days. Alright, so here if you see on day 1 Day
2 Day 8 day 9 and 11. The Outlook has been Alright, so basically we try
to plaster a data set depending on the Outlook. So when the Outlook is sunny, this is our data set
when the Outlook is overcast. This is what we have and when the Outlook is
the rain this is what we have. All right, so when it is sunny we have
two yeses and three nodes. Okay, when the
Outlook is overcast. We have all four
as yes has meaning that on the four days
when the Outlook was overcast. We can play the game. All right. Now when it comes to drain, we have three yeses
and two nodes. All right. So if you notice here, the decision is being made by
choosing the Outlook variable as the root node. Okay. So the root node is basically the topmost node
in a decision tree. Now, what we've done here is
we've created a decision tree that starts with
the Outlook node. All right, then you're splitting
the decision tree further depending on other parameters
like Sunny overcast and rain. All right now like we know
that Outlook has three values. Sunny overcast and brain
so let me explain this in a more in-depth manner. Okay. So what you're doing
here is you're making the decision Tree by choosing
the Outlook variable at the root node. The root note is
basically the topmost node in a decision tree. Now the Outlook node has three
branches coming out from it, which is sunny
overcast and rain. So basically Outlook can have three values
either it can be sunny. It can be overcast
or it can be rainy. Okay now these three values
Use are assigned to the immediate Branch
nodes and for each of these values the possibility of play is equal
to yes is calculated. So the sunny and the rain branches
will give you an impure output. Meaning that there is a mix
of yes and no right. There are two yeses
here three nodes here. There are three yeses here
and two nodes over here, but when it comes
to the overcast variable, it results in a hundred
percent pure subset. All right, this shows that
the overcast baby. Will result in a definite
and certain output. This is exactly what entropy
is used to measure. All right, it calculates
the impurity or the uncertainty. Alright, so the lesser
the uncertainty or the entropy of a variable more
significant is that variable? So when it comes to overcast
there's literally no impurity in the data set. It is a hundred percent
pure subset, right? So be want variables like these
in order to build a model. All right now, we don't always Ways get lucky
and we don't always find variables that will result
in pure subsets. That's why we have
the measure entropy. So the lesser the entropy of
a particular variable the most significant that variable
will be so in a decision tree. The root node is assigned
the best attribute so that the decision tree
can predict the most precise outcome meaning
that on the root note. You should have the most
significant variable. All right, that's why
we've chosen Outlook or and now some of you might ask
me why haven't you chosen overcast Okay is overcast
is not a variable. It is a value
of the Outlook variable. All right. That's why we've chosen
outlook here because it has a hundred percent pure subset
which is overcast. All right. Now the question in your head is
how do I decide which variable or attribute best Blitz
the data now right now, I know I looked at the data and I told you that, you know here we have
a hundred percent pure subset, but what if it's
a more complex problem and you're not able
to understand which variable will best split the data, so guys when it comes to decision tree
Information and gain and entropy will help you understand which variable
will best split the data set. All right, or which variable you
have to assign to the root node because whichever variable
is assigned to the dude node. It will best let the data set and it has to be the most
significant variable. All right. So how we can do this
is we need to use Information Gain and entropy. So from the total
of the 14 instances that we saw nine
of them said yes and 5 of the instances said know that you cannot play
on that particular day. All right. So how do you
calculate the entropy? So this is the formula
you just substitute the values in the formula. So when you substitute
the values in the formula, you will get a value of 0.9940. All right. This is the entropy or this is the uncertainty
of the data present in a sample. Now in order to ensure that we choose the best variable
for the root node. Let us look at all
the possible combinations that you can use
on the root node. Okay, so these are All
the possible combinations you can either have
Outlook you can have windy humidity or temperature. Okay, these are four variables
and you can have any one of these variables
as your root node. But how do you select which variable best
fits the root node? That's what we are going
to see by using Information Gain and entropy. So guys now the task at hand
is to find the information gain for each of these attributes. All right. So for Outlook for windy for
humidity and for temperature, we're going to find
out the information. Nation gained right now
a point to remember is that the variable that results in the highest
Information Gain must be chosen because it will give us the most
precise and output information. All right. So the information gain for
attribute windy will calculate that first here. We have six instances of true
and eight instances of false. Okay. So when you substitute all
the values in the formula, you will get a value
of zero point zero four eight. So we get a value
of You 2.0 for it. Now. This is a very low value
for Information Gain. All right, so the information that you're going to get from
Windy attribute is pretty low. So let's calculate
the information gain of attribute Outlook. All right, so from the total
of 14 instances, we have five instances
with say Sunny for instances, which are overcast
and five instances, which are rainy. All right for Sonny. We have three yeses and to nose
for overcast we have All the for as yes for any we have
three years and two nodes. Okay. So when you calculate
the information gain of the Outlook variable
will get a value of zero point 2 4 7 now compare
this to the information gain of the windy attribute. This value is
actually pretty good. Right we have zero point 2 4 7
which is a pretty good value for Information Gain. Now, let's look
at the information gain of attribute humidity
now over here. We have seven instances
with say hi and seven instances with say normal. Right and under
the high Branch node. We have three instances
with say yes, and the rest for instances
would say no similarly under the normal Branch. We have one two, three, four, five six seven
instances would say yes and one instance with says no. All right. So when you calculate
the information gain for the humidity variable, you're going to get
a value of 0.15 one. Now. This is also
a pretty decent value, but when you compare it
to the Information Gain, Of the attribute Outlook it
is less right now. Let's look at the information
gain of attribute temperature. All right, so the temperature
can hold repeat. So basically the temperature
attribute can hold hot mild and cool. Okay under hot. We have two instances
with says yes and two instances for no under mild. We have four instances of yes
and two instances of no and under col we have
three instances of yes and one instance of no. All right. When you calculate the information gain
for this attribute, you will get a value
of zero point zero to nine, which is again very less. So what you can summarize
from here is if we look at the information gain for each
of these variable will see that for Outlook. We have the maximum gain. All right, we have
zero point two four seven, which is the highest
Information Gain value and you must always choose
a variable with the highest Information Gain to split
the data at the root node. So that's why we assign
The Outlook variable at the root node. All right, so guys. I hope this use case with clear
if any of you have doubts. Please keep commenting
those doubts now, let's move on and look at what
exactly a confusion Matrix is the confusion Matrix
is the last topic for descriptive statistics
read after this. I'll be running a short demo
where I'll be showing you how you can calculate
mean median mode and standard deviation variance
and all of those values by using our okay. So let's talk about
confusion Matrix now guys. What is the confusion Matrix
now don't get confused. This is not any complex
topic now confusion. Matrix is a matrix that is often used to describe
the performance of a model. All right, and this
is specifically used for classification models or a classifier and what it does is it
will calculate the accuracy or it will calculate the
performance of your classifier by comparing your actual results
and Your predicted results. All right. So this is what it looks like to positive to
negative and all of that. Now this is a little confusing. I'll get back to what
exactly true positive to negative and all
of this stands for for now. Let's look at an example and
let's try and understand what exactly confusion Matrix is. So guys have made sure that I put examples
after each and every topic because it's important you understand the Practical
part of Statistics. All right statistics has
literally nothing to do with Theory you need
to understand how Calculations are done in statistics. Okay. So here what I've done is now
let's look at a small use case. Okay, let's consider that your given data
about a hundred and sixty five patients out of which hundred
and five patients have a disease and the remaining 50 patients
don't have a disease. Okay. So what you're going to do is
you will build a classifier that predicts by using these hundred and
sixty five observations. You'll feed all of these 165 observations
to your classifier and it will predict
the output every time a new patients detail is fed
to the classifier right now out of these 165 cases. Let's say that
the classifier predicted. Yes hundred and ten times
and no 55 times. Alright, so yes
basically stands for yes. The person has a disease
and no stands for know. The person does
not have a disease. All right, that's
pretty self-explanatory. But yeah, so it predicted
that a hundred and ten times. Patient has a disease
and 55 times that know the patient
doesn't have a disease. However in reality only
hundred and five patients in the sample have
the disease and 60 patients who do not have
the disease, right? So how do you calculate
the accuracy of your model? You basically build
the confusion Matrix? All right. This is how the Matrix looks like and basically denotes
the total number of observations that you have which is 165 in our case
actual denotes the actual use in the data set and predicted denotes
the predicted values by the classifier. So the actual value is no here and the predicted
value is no here. So your classifier
was correctly able to classify 50 cases as no. All right, since both
of these are no so 50 it was correctly able
to classify but 10 of these cases it
incorrectly classified meaning that your actual value here
is no but you classifier predicted it as yes or I that's why this
And over here similarly it wrongly predicted that five patients
do not have diseases whereas they actually
did have diseases and it correctly
predicted hundred patients, which have the disease. All right. I know this is
a little bit confusing. But if you look
at these values no, no 50 meaning that it correctly
predicted 50 values No Yes means that it
wrongly predicted. Yes for the values are it
was supposed to predict. No. All right. Now what exactly is? Is this true positive
to negative and all of that? I'll tell you what
exactly it is. So true positive are the cases
in which we predicted a yes and they do not actually
have the disease. All right, so it is
basically this value already predicted a yes here, even though they
did not have the disease. So we have 10 true positives
right similarly true- is we predicted know and they don't have
the disease meaning that this is correct. False positive is be predicted. Yes, but they do not
actually have the disease or at this is also known as type
1 error falls- is we predicted. No, but they actually
do not have the disease. So guys basically falls- and true negatives are basically
correct classifications. All right. So this was confusion Matrix and I hope this concept
is clear again guys. If you have doubts, please comment your doubt
in the comment section. So guys, that was
the entire descriptive. X module and now we
will discuss about probability. Okay. So before we understand
what exactly probability is, let me clear out a very
common misconception people often tend to ask
me this question. What is the relationship between
statistics and probability? So probability and statistics
are related fields. All right. So probability is
a mathematical method used for statistical analysis. Therefore we can say that a probability and
statistics are interconnected. Launches of mathematics that deal with analyzing the
relative frequency of events. So they're very
interconnected feels and probability makes
use of statistics and statistics makes use of probability or a they're
very interconnected Fields. So that is the relationship
between statistics and probability. Now, let's understand
what exactly is probability. So probability is the measure of How likely an event
will occur to be more precise. It is the ratio. Of desired outcome
to the total outcomes. Now, the probability of all outcomes always sum up
to 1 the probability will always sum up to 1 probability
cannot go beyond one. Okay. So either your probability
can be 0 or it can be 1 or it can be in the form
of decimals like 0.5 to or 0.55 or it can be
in the form of 0.5 0.7 0.9. But it's valuable always stay
between the range 0 and 1. Okay at the famous example of probability is rolling
a dice example. So when you roll a dice you get
six possible outcomes, right? You get one two, three four and five six
phases of a dice now each possibility only
has one outcome. So what is the probability
that on rolling a dice? You will get 3 the probability
is 1 by 6, right because there's only one phase which has the number 3 on it
out of six phases. There's only one phase
which has the number three. So the probability of getting 3 when you roll a dice
is 1 by 6 similarly, if you want to find
the probability of getting a number 5 again, the probability is
going to be 1 by 6. All right, so all
of this will sum up to 1. All right, so guys this is
exactly what probability is. It's a very simple concept
we all learnt it in 8 standard onwards right now. Let's understand the
different terminologies that are related to probability. Now the three terminologies
that you often come across when We talk about probability. We have something known
as the random experiment. Okay, it's basically
an experiment or a process for which the outcomes cannot be
predicted with certainty. All right. That's why you use probability. You're going to use probability
in order to predict the outcome with some sort of certainty sample space
is the entire possible set of outcomes of a random
experiment an event is one or more outcomes
of an experiment. So if you consider the example
Love rolling a dice. Now. Let's say that you want
to find out the probability of getting a to
when you roll the dice. Okay. So finding this probability
is the random experiment the sample space is basically
your entire possibility. Okay. So one two, three, four, five six phases are there
and out of that you need to find the probability
of getting a 2, right. So all the possible outcomes will basically represent
your sample space. Okay. So 1 to 6 are all your possible
outcomes this represents. Sample space event is
one or more outcome of an experiment. So in this case
my event is to get a to when I roll a dice, right? So my event is the probability
of getting a to when I roll a dice. So guys, this is basically what
random experiment sample space and event really means alright. Now, let's discuss
the different types of events. There are two types of events
that you should know about there is disjoint and non disjoint
events disjoint events. These are events that do not have
any common outcome. For example, if you draw a single card
from a deck of cards, it cannot be a king
and a queen correct. It can either be king
or it can be Queen. Now a non disjoint
events are events that have common outcomes. For example, a student
can get hundred marks in statistics and hundred
marks in probability. All right, and also the outcome of a ball delibird
can be a no ball and it can be a 6 right. So this is Non
disjoint events are or n. These are very simple
to understand right now. Let's move on and look
at the different types of probability distribution. All right, I'll be discussing the three main probability
distribution functions. I'll be talking
about probability density function normal distribution
and Central limit theorem. Okay probability density
function also known as PDF is concerned with the relative likelihood for
a continuous random variable. To take on a given value. All right. So the PDF gives the probability of a variable that lies
between the range A and B. So basically what you're trying
to do is you're going to try and find the probability
of a continuous random variable over a specified range. Okay. Now this graph denotes the PDF
of a continuous variable. Now, this graph is also known
as the bell curve right? It's famously called
the bell curve because of its shape and there are
three important properties that you To know about
a probability density function. Now the graph of a PDF
will be continuous over a range. This is because you're
finding the probability that a continuous variable lies
between the ranges A and B, right the second property is that the area bounded by
the curve of a density function and the x-axis is equal
to 1 basically the area below the curve is equal
to 1 all right, because it denotes
probability again the probability cannot arrange. More than one it has to be between 0 and 1 property number
three is that the probability that our random variable
assumes a value between A and B is equal to the area under the PDF bounded
by A and B. Okay. Now what this means is that the probability value
is denoted by the area of the graph. All right, so whatever value
that you get here, which basically one
is the probability that a random variable will lie
between the range A and B. All right, so I hope If you have understood the
probability density function, it's basically the probability
of finding the value of a continuous random variable
between the range A and B. All right. Now, let's look
at our next distribution, which is normal distribution
now normal distribution, which is also known as the gaussian distribution is
a probability distribution that denotes the
symmetric property of the mean right meaning that the idea
behind this function is that The data near the mean occurs more frequently than
the data away from the mean. So what it means to say is that the data around the mean
represents the entire data set. Okay. So if you just take
a sample of data around the mean it can represent
the entire data set now similar to the probability density
function the normal distribution appears as a bell curve. All right. Now when it comes
to normal distribution, there are two important factors. All right, we have the mean
of the population. And the standard deviation. Okay, so the mean and the graph
determines the location of the center of the graph, right and the standard deviation
determines the height of the graph. Okay. So if the standard deviation
is large the curve is going to look something like this. All right, it'll be
short and wide and if the standard deviation
is small the curve is tall and narrow. All right. So this was it
about normal distribution. Now, let's look
at the central limit theorem. Now the central
limit theorem states that the sampling distribution of the mean of any independent
random variable will be normal or nearly normal if the sample size
is large enough now, that's a little confusing. Okay. Let me break it down for
you now in simple terms if we had a large population and we divided it
into many samples. Then the mean of all the samples from the population
will be almost equal to the mean of the entire
population right meaning that each of the sample
is normally distributed. Right. So if you compare the mean
of each of the sample, it will almost be equal
to the mean of the population. Right? So this graph basically shows
a more clear understanding of the central limit theorem red
you can see each sample here and the mean of each sample is almost
along the same line, right? Okay. So this is exactly what the central limit theorem
States now the accuracy or the resemblance to the normal distribution
depends on two main factors. Right. So the first is the number
of sample points that you consider. All right, and the second is a shape
of the underlying population. Now the shape obviously depends
on the standard deviation and the mean
of a sample, correct. So guys the central
limit theorem basically states that each sample
will be normally distributed in such a way that the mean of each sample
will coincide with the mean of the actual population. All right in short terms. That's what central
limit theorem States. Alright, and this
holds true only for a large. Is it mostly
for a small data set and there are more deviations when compared to a large
data set is because of the scaling Factor, right? The small is deviation in a small data set will change
the value very drastically, but in a large data
set a small deviation will not matter at all. Now, let's move on and look
at our next topic which is the different
types of probability. Now, this is a important topic because most of your problems
can be solved by understanding which type of probability
should I use to solve? This problem right? So we have three important
types of probability. We have marginal joint
and conditional probability. So let's discuss each of these now the probability of
an event occurring unconditioned on any other event is known
as marginal probability or unconditional probability. So let's say that you want
to find the probability that a card drawn is a heart. All right. So if you want to
find the probability that a card drawn is
a heart the prophet. B13 by 52 since there
are 52 cards in a deck and there are 13 hearts
in a deck of cards. Right and there are
52 cards in a turtleneck. So your marginal probability
will be 13 by 52. That's about
marginal probability. Now, let's understand. What is joint probability. Now joint probability is a measure of two events
happening at the same time. Okay. Let's say that the two
events are A and B. So the probability of event A and B occurring is
the dissection of A and B. So for example, if you want to
find the probability that a card is a four and a red
that would be joint probability. All right, because
you're finding a card that is 4 and the card
has to be red in color. So for the answer, this will be 2 by 52
because we have 1/2 in heart and we have 1/2
and diamonds correct. So both of these are red
and color therefore. Our probability is to by 52 and if you further down
it Is 1 by 26, right? So this is what
joint probability is all about moving on. Let's look at what exactly
conditional probability is. So if the probability of an event or an outcome
is based on the occurrence of a previous event
or an outcome, then you call it as
a conditional probability. Okay. So the conditional probability
of an event B is the probability that the event will occur given that an event a has
already occurred, right? So if a and b are
dependent events, then the expression for conditional probability
is given by this. Now this first term
on the left hand side, which is p b of a is
basically the probability of event B occurring given that event a
has already occurred. All right. So like I said, if a and b are dependent events, then this is
the expression but if a and b are independent events, and the expression
for conditional probability is like this, right? So guys P of A and B of B is
obviously the probability of A and probability of B right now. Let's move on now in order to understand conditional
probability joint probability and marginal probability. Let's look at a small use case. Okay now basically
we're going to take a data set which examines the salary
package and training undergone my candidates. Okay. Now in this there are
60 candidates without training and forty five candidates, which have enrolled for
Adder a curse training. Right. Now the task here is you have
to assess the training with a salary package. Okay, let's look at this
in a little more depth. So in total, we have hundred and five
candidates out of which 60 of them have not enrolled
Frederick has training and 45 of them have enrolled
for a deer Acres training or this is a small survey that was conducted and this is the rating
of the package or the salary that they got right? So if you read through the data, you can understand
there were five candidates. It's without education or training who got a very
poor salary package. Okay. Similarly, there are 30 candidates with
Ed Eureka training who got a good package, right? So guys basically you're
comparing the salary package of a person depending on whether or not they've enrolled
for a director training, right? This is our data set. Now, let's look at our problem
statement find the probability that a candidate
has undergone a Drake has training quite simple, which type of probability
is this Is this is marginal probability? Right? So the probability that a candidate has undergone
edger Acres training is obviously 45 divided
by a hundred and five since 45 is the number of candidates with
Eddie record raining and hundred and five is
the total number of candidates. So you get a value
of approximately 0.4 to all right, that's the probability
of a candidate that has undergone educate
a girl straining next question find the probability that a candidate has attended
edger Acres training. Also has good package. Now. This is obviously a joint
probability problem, right? So how do you
calculate this now? Since our table is quite
formatted we can directly find that people who have
gotten a good package along with Eddie record
raining or 30, right? So out of hundred and
five people 30 people have education training
and a good package, right? They specifically
asking for people with Eddie record raining. Remember that night. The question is find the
probability that a gang Today, it has attended
editor Acres training and also has a good package. All right, so we need
to consider two factors that is a candidate who's addenda deaderick
has training and who has a good package. So clearly that number is 30 30 divided by
total number of candidates, which is 1:05, right? So here you get
the answer clearly next. We have find the probability that a candidate has
a good package given that he has not
undergone training. Okay. Now this is Early
conditional probability because here you're defining
a condition you're saying that you want to find
the probability of a candidate who has a good package given
that he's not undergone. Any training, right? The condition is that he's
not undergone any training. All right. So the number of people who have not undergone
training are 60 and out of that five of them
have got a good package that so that's why this is Phi
by 60 and not five by a hundred and five because here they have clearly
mentioned has a good pack. Given that he has
not undergone training. So you have to only consider people who have
not undergone training, right? So any five people who have not undergone
training have gotten a good package, right? So 5 divided by 60 you get
a probability of around 0.08 which is pretty low, right? Okay. So this was all about the different types
of probability now, let's move on and look at
our last Topic in probability, which is base theorem. Now guys base. Your room is a very
important concept when it comes to statistics and probability. It is majorly used
in knife bias algorithm. Those of you who aren't aware. Now I've bias is a supervised
learning classification algorithm and it is mainly used
in Gmail spam filtering right? A lot of you might have noticed
that if you open up Gmail, you'll see that you have
a folder called spam right or that is carried out
through machine learning and And the algorithm use
there is knife bias, right? So now let's discuss what
exactly the Bayes theorem is and what it denotes
the bias theorem is used to show the relation between
one conditional probability and it's inverse. All right. Basically it's nothing
but the probability of an event occurring based
on prior knowledge of conditions that might be related
to the same event. Okay. So mathematically the bell's
theorem is represented like this right now. Shown in this equation. The left-hand term is referred
to as the likelihood ratio which measures the probability of occurrence of event
be given an event a okay on the left hand side is what is known as
the posterior right is referred to as posterior, which means that the probability of occurrence of a given
an event be right. The second term is referred to as the likelihood Ratio or at
this measures the probability of occurrence of B
given an event. A now P of a is also
known as the prior which refers to the actual
probability distribution of A and P of B is again, the probability of B, right. This is the bias theorem and in order to better
understand the base theorem. Let's look at a small example. Let's say that we have
three bowels we have bow is a bow will be and bouncy. Okay barley contains
two blue balls and for red balls bowel B contains eight blue
balls and for red balls. Wow Zeke. Games one blue ball
and three red balls now if we draw one ball
from each Bowl, what is the probability
to draw a blue ball from a bowel a if we know that we drew exactly a total
of two blue balls, right? If you didn't
understand the question, please read it I shall pause
for a second or two. Right. So I hope all of you
have understood the question. Okay. Now what I'm going to do
is I'm going to draw a blueprint for you and tell you how exactly
to solve the problem. But I want you all to give
me the solution to this problem, right? I'll draw a blueprint. I'll tell you
what exactly the steps are but I want you to come
up with a solution on your own right the formula
is also given to you. Everything is given to you. All you have to do is come up
with the final answer. Right? Let's look at how you
can solve this problem. So first of all, what we will do is
Let's consider a all right, let a be the event of picking a blue ball
from bag in and let X be the event of picking
exactly two blue balls, right because these
are the two events that we need to calculate
the probability of now there are two probabilities
that you need to consider here. One is the event of picking
a blue ball from bag a and the other is the event of
picking exactly two blue balls. Okay. So these two are represented
by a and X respectively and so what we want is
the probability of occurrence of event a given X, which means that given that we're picking
exactly two blue balls. What is the probability that we are picking
a blue ball from bag? So by the definition
of conditional probability, this is exactly what
our equation will look like. Correct. This is basically a occurrence
of event a given element X and this is
the probability of a and x and this is the probability
of X alone, correct. What we need to do is we need
to find these two probabilities which is probability of a
and X occurring together and probability of X. Okay. This is the entire solution. So how do you find P probability of X this you can do
in three ways. So first is white ball
from a either white from be or read from see now first is
to find the probability of x x basically represents the event of picking exactly
two blue balls. Right. So these are the three ways
in which it is possible. So you'll pick one blue ball
from bowel a and one from bowel be in the second case. You can pick one from a and another blue ball
from see in the third case. You can pick a blue
ball from Bagby and a blue ball from bagsy. Right? These are the three ways
in which it is possible. So you need to find
the probability of each of this step do is that you need to find
the probability of a and X occurring together. This is the sum
of terms one and two. Okay, this is because in both
of these events, you're picking a ball
from bag, correct? So there is find out
this probability and let me know your answer
in the comment section. All right. We'll see if you get
the answer right? I gave you the entire
solution to this. All you have to do is
substitute the value right? If you want a second or two, I'm going to pause on the screen
so that you can go through this in a more clearer way right? Remember that you need
to calculate two. He's the first probability that you need to calculate is
the event of picking a blue ball from bag a given that you're picking
exactly two blue balls. Okay, II probability you need
to calculate is the event of picking exactly to bluebirds. All right. These are the two probabilities. You need to calculate so
remember that and this is the solution. All right, so guys, make sure you
mention your answers in the comment section for now. Let's move on and Get
our next topic, which is the
inferential statistics. So guys, we just completed the
probability module right now. We will discuss
inferential statistics, which is the second
type of Statistics. We discussed descriptive
statistics earlier. All right. So like I mentioned earlier
inferential statistics also known as statistical inference
is a branch of Statistics that deals with forming
inferences and predictions about a population based
on a sample of data. Taken from the population. All right, and the question
you should ask is how does one form inferences
or predictions on a sample? The answer is you
use Point estimation? Okay. Now you must be wondering what is point estimation
one estimation is concerned with the use of the sample data
to measure a single value which serves as
an approximate value or the best estimate of
an unknown population parameter. That's a little confusing. Let me break it down
to you for Camping in order to calculate the mean
of a huge population. What we do is we first draw out
the sample of the population and then we find the sample mean right the sample mean
is then used to estimate the population mean this is
basically Point estimate, you're estimating the value
of one of the parameters of the population, right? Basically the main you're trying to estimate
the value of the mean. This is what point estimation is
the two main terms in point estimation. There's something known as as the estimator
and the something known as the estimate estimator
is a function of the sample that is used to find
out the estimate. Alright in this example. It's basically the sample
mean right so a function that calculates the sample
mean is known as the estimator and the realized value of the estimator is
the estimate right? So I hope Point
estimation is clear. Now, how do you
find the estimates? There are four common ways
in which you can do this. The first one is
method of Moment yo, what you do is
you form an equation in the sample data set and then you analyze
the similar equation in the population data set as well like the population mean
population variance and so on. So in simple terms, what you're doing is you're
taking down some known facts about the population and you're extending
those ideas to the sample. Alright, once you do that, you can analyze the sample
and estimate more essential or more complex
values right next. We have maximum likelihood. This method basically uses
a model to estimate a value. All right. Now a maximum likelihood
is majorly based on probability. So there's a lot of probability
involved in this method next. We have the base estimator
this works by minimizing the errors or the average risk. Okay, the base estimator
has a lot to do with the Bayes theorem. All right, let's
not get into the depth of these estimation methods. Finally. We have the best unbiased
estimators in this method. There are seven unbiased
estimators that can be used to approximate a parameter. Okay. So Guys these were
a couple of methods that are used
to find the estimate but the most well-known method
to find the estimate is known as the interval estimation. Okay. This is one of the most important
estimation methods right? This is where confidence
interval also comes into the picture right apart
from interval estimation. We also have something
known as margin of error. So I'll be discussing
all of this. In the upcoming slides. So first let's understand. What is interval estimate? Okay, an interval
or range of values, which are used to estimate a
population parameter is known as an interval estimation, right? That's very understandable. Basically what they're trying to
see is you're going to estimate the value of a parameter. Let's say you're trying to find
the mean of a population. What you're going to do is
you're going to build a range and your value will lie in
that range or in that interval. Alright, so this way your output
is going to be more accurate because you've not predicted
a point estimation instead. You have estimated an interval within which your value
might occur, right? Okay. Now this image clearly shows how Point estimate and interval
estimate or different so guys interval estimate
is obviously more accurate because you are not just
focusing on a particular value or a particular point in order to predict
the probability instead. You're saying that
the value might be within this range between
the lower confidence limit and the upper confidence limit. All right, this is denotes
the range or the interval. Okay, if you're still confused
about interval estimation, let me give you a small example if I stated that I will take
30 minutes to reach the theater. This is known
as Point estimation. Okay, but if I stated that I will take
between 45 minutes to an hour to reach the theater. This is an example
of into Estimation. All right. I hope it's clear. Now now interval estimation
gives rise to two important statistical terminologies one
is known as confidence interval and the other is known
as margin of error. All right. So there's it's important that you pay attention to both of these terminologies
confidence interval is one of the most significant measures that are used to check how essential machine
learning model is. All right. So what is confidence interval
confidence interval is the measure of your confidence that the interval
estimated contains the population parameter
or the population mean or any of those parameters
right now statisticians use confidence interval
to describe the amount of uncertainty associated with the sample estimate of
a population parameter now guys, this is a lot of definition. Let me just make you
understand confidence interval with a small example. Okay. Let's say that you perform a survey and you survey
a group of cat owners. The see how many cans
of cat food they purchase in one year. Okay, you test your statistics at the 99
percent confidence level and you get
a confidence interval of hundred comma 200 this means that you think that the cat owners by between hundred to two
hundred cans in a year and also since the confidence
level is 99% shows that you're very confident
that the results are, correct. Okay. I hope all of you
are clear with that. Alright, so your confidence
interval here will be a hundred and two hundred and your confidence level
will be 99% Right? That's the difference
between confidence interval and confidence level So
within your confidence interval your value is going to lie and
your confidence level will show how confident you are
about your estimation, right? I hope that was clear. Let's look at margin of error. No margin of error for a given level of confidence
is a greatest possible distance between the Point estimate and the value of the parameter that it is estimating
you can say that it is a deviation from
the actual point estimate right. Now. The margin of error
can be calculated using this formula now zc
her denotes the critical value or the confidence interval and this is X standard
deviation divided by root of the sample size. All right, n is basically
the sample size now, let's understand how
you can estimate the confidence intervals. So guys the level of confidence which is denoted by
C is the probability that the interval estimate
contains a population parameter. Let's say that you're trying
to estimate the mean. All right. So the level of confidence
is the probability that the interval
estimate contains the population parameter. So this interval
between minus Z and z or the area beneath this curve
is nothing but the probability that the interval estimate
contains a population parameter. You don't all right. It should basically
contain the value that you are predicting right. Now. These are known
as critical values. This is basically
your lower limit and your higher
limit confidence level. Also, there's something
known as the Z score now. This court can be calculated by
using the standard normal table. All right, if you look
it up anywhere on Google you'll find the z-score table or the standard normal
table to understand how this is done. Let's look at a small example. Okay, let's say
that the level of confidence. Vince is 90% This means
that you are 90% confident that the interval contains
the population mean. Okay, so the remaining 10%
which is out of hundred percent. The remaining 10%
is equally distributed on these tail regions. Okay, so you have 0.05 here
and 0.05 over here, right? So on either side of see you will distribute
the other leftover percentage now these Z scores
are calculated from the table as I mentioned before. All right one. I'm 6 4 5 is get collated
from the standard normal table. Okay, so guys how you estimate
the level of confidence? So to sum it up. Let me tell you the steps that
are involved in constructing a confidence interval first. You would start by identifying
a sample statistic. Okay. This is the statistic that you will use to estimate
a population parameter. This can be anything
like the mean of the sample next you
will select a confidence level now the confidence level
describes the uncertainty of a Sampling method right after that you'll find
something known as the margin of error right? We discussed margin
of error earlier. So you find this based
on the equation that I explained
in the previous slide, then you'll finally specify
the confidence interval. All right. Now, let's look
at a problem statement to better understand
this concept a random sample of 32 textbook prices is taken
from a local College Bookstore. The mean of the sample is so so and so and the sample
standard deviation is This use a 95% confident level and find the margin
of error for the mean price of all text books
in the bookstore. Okay. Now, this is a very
straightforward question. If you want you can read
the question again. All you have to do is you have
to just substitute the values into the equation. All right, so guys, we know the formula for margin
of error you take the Z score from the table. After that we have deviation
Madrid's 23.4 for right and that's standard deviation
and n stands for the number of samples here. The number of samples is
32 basically 32 textbooks. So approximately your margin
of error is going to be around 8.1 to this is
a pretty simple question. All right. I hope all of you
understood this now that you know, the idea behind
confidence interval. Let's move ahead to one of the most important topics
in statistical inference, which is hypothesis
testing, right? So Ugly statisticians
use hypothesis testing to formally check whether the hypothesis
is accepted or rejected. Okay, hypothesis. Testing is an inferential
statistical technique used to determine whether there is enough evidence
in a data sample to infer that a certain condition holds
true for an entire population. So to understand the characteristics
of a general population, we take a random sample, and we analyze the properties
of the sample right we test. Whether or not the identified
conclusion represents the population accurately and finally we interpret
the results now whether or not to accept
the hypothesis depends upon the percentage value
that we get from the hypothesis. Okay, so to
better understand this, let's look at a small
example before that. There are a few steps
that are followed in hypothesis testing you begin
by stating the null and the alternative hypothesis. All right. I'll tell you what
exactly these terms are and then you formulate. Analysis plan right after that
you analyze the sample data and finally you can
interpret the results right now to understand
the entire hypothesis testing. We look at a good example. Okay now consider
for boys Nick jean-bob and Harry these boys
were caught bunking a class and they were asked
to stay back at school and clean the classroom
as a punishment, right? So what John did is he decided that four of them would take
turns to clean their classrooms. He came up with a plan
of writing each of their names on chits and putting them in a bowl now every day they had
to pick up a name from the bowel and that person had to play
in the clock, right? That sounds pretty fair
enough now it is been three days and everybody's name has come up
except John's assuming that this event
is completely random and free of bias. What is a probability of John not cheating
right or is the probability that he's not actually
cheating this can Solved by using hypothesis testing. Okay. So we'll Begin by calculating
the probability of John not being picked for a day. Alright, so we're
going to assume that the event is free of bias. So we need to find
out the probability of John not cheating right first
we will find the probability that John is not picked
for a day, right? We get 3 out of 4, which is basically 75%
75% is fairly high. So if John is not picked
for three days in a row the Probability will drop down
to approximately 42% Okay. So three days in a row meaning that is the probability
drops down to 42 percent. Now, let's consider a situation where John is not picked
for 12 days in a row the probability drops down
to three point two percent. Okay. That's the probability
of John cheating becomes fairly high, right? So in order for statisticians to come
to a conclusion, they Define what is known
as a threshold value. Right considering
the above situation if the threshold value
is set to 5 percent. It would indicate that if the probability lies
below 5% then John is cheating his way out of detention. But if the probability is
about threshold value then John it just lucky and his name
isn't getting picked. So the probability and hypothesis testing give rise
to two important components of hypothesis testing, which is null hypothesis
and alternative hypothesis. Null. Hypothesis is based. Basically approving the Assumption alternate
hypothesis is when your result disapproves
the Assumption right therefore in our example, if the probability
of an event occurring is less than 5% which it is
then the event is biased hence. It proves the
alternate hypothesis. So guys with this we come
to the end of this session. Let's go ahead
and understand what exactly is. Was learning so
supervised learning is where you have
the input variable X and the output variable Y
and use an algorithm to learn the map Egg function
from the input to the output as I mentioned earlier with the example
of face detection. So it is called
supervised learning because the process
of an algorithm learning from the training data
set can be thought of as a teacher supervising
the learning process. So if we have a look
at the supervised learning steps or What would rather
say the workflow? So the model is used
as you can see here. We have the historic data. Then we again we have
the random sampling. We split the data
into train your asset and the testing data set using
the training data set. We with the help
of machine learning which is supervised
machine learning. We create statistical model then after we have a mod
which is being generated with the help
of the training data set. What we do is use
the testing data set for production and testing. What we do is get the output and finally we have
the model validation outcome. That was the
training and testing. So if we have a look
at the prediction part of any particular supervised
learning algorithm, so the model is used
for operating outcome of a new data set. So whenever performance of the model degraded
the model is retrained or if there are
any performance issues, the model is retained with
the help of the new data now when we talk about supervisor in there not just one
but quite a few algorithms here. So we have linear
regression logistic regression. This is entry. We have random Forest. We have made by classifiers. So linear regression is used
to estimate real values. For example, the cost of houses. The number of calls
the total sales based on the continuous variables. So that is what
reading regression is. Now when we talk
about logistic regression, which is used to estimate
discrete values, for example, which are binary values
like 0 and 1 yes, or no true. False based on the given set
of independent variables. So for example, when you are talking
about something like the chances of winning or if you talk about winning which can be
either true or false if will it rain today
with it can be the yes or no, so it cannot be like when the output
of a particular algorithm or the particular
question is either. Yes. No or Banner e then only we use a large
stick regression the next we have decision trees. So now these are used for
classification problems it work. X for both
categorical and continuous dependent variables and if we talk about random Forest
So Random Forest is an M symbol of a decision tree, it gives better prediction
accuracy than decision tree. So that is another type of
supervised learning algorithm. And finally we have
the need based classifier. So it was a classification technique based
on the Bayes theorem with an assumption of
Independence between predictors. A linear regression is one
of the easiest algorithm in machine learning. It is a statistical model that attempts to
show the relationship between two variables
with a linear equation. But before we drill down to linear regression
algorithm in depth, I'll give you a quick overview
of today's agenda. So we'll start a session with a quick overview
of what is regression as linear regression
is one of a type of regression algorithm. Once we learn about regression, its use case the various
types of it next. We'll learn about
the algorithm from scratch. Each where I'll teach you it's mathematical
implementation first, then we'll drill down
to the coding part and Implement linear
regression using python in today's session will deal with linear regression algorithm
using least Square method check its goodness of fit or how close the data is to the fitted regression line
using the R square method. And then finally what will do will optimize it
using the gradient decent method in the last part
on the coding session. I'll teach you to implement
linear regression using Python and Coding session would be divided into two parts
the first part would consist of linear regression
using python from scratch where you will use
the mathematical algorithm that you have learned
in this session. And in the next part
of the coding session will be using scikit-learn
for direct implementation of linear regression. So let's begin our session
with what is regression. Well regression analysis is a form of predictive
modeling technique which investigates the
relationship between a dependent and independent variable
a regression analysis. Vols graphing a line
over a set of data points that most closely fits
the overall shape of the data or regression shows the changes
in a dependent variable on the y-axis to the changes in the explanation variable
on the x-axis fine. Now you would ask
what are the uses of regression? Well, there are major three uses
of regression analysis the first being determining the strength
of predicates errs, the regression might be used
to identify the strength of the effect that the independent variables
have on the dependent variable or But you can ask question. Like what is the strength
of relationship between sales and marketing spending or what
is the relationship between age and income second is forecasting an effect in this the regression
can be used to forecast effects or impact of changes. That is the regression analysis
help us to understand how much the dependent variable
changes with the change and one or more
independent variable fine. For example, you can ask
question like how much additional say Lancome
will I get for each? Thousand dollars
spent on marketing. So it is Trend forecasting in this the regression
analysis predict Trends and future values. The regression analysis can
be used to get Point estimates in this you can ask questions. Like what will be
the price of Bitcoin and next six months, right? So next topic is linear versus
logistic regression by now. I hope that you know,
what a regression is. So let's move on
and understand its type. So there are various kinds
of regression like linear regression logistic regression
polynomial regression. Others only but for this session will be focusing on linear
and logistic regression. So let's move on and let me tell
you what is linear regression. And what is logistic regression then what we'll do
we'll compare both of them. All right. So starting with
linear regression in simple linear regression, we are interested in things
like y equal MX plus C. So what we are trying to find
is the correlation between X and Y variable this means that every value of x has a corresponding
value of y and it if it is continuous. All right, however
in logistic regression, we are not fitting our data
to a straight line like linear regression instead
what we are doing. We are mapping Y versus X to a sigmoid function
in logistic regression. What we find out is is y 1 or 0
for this particular value of x so thus we are essentially
deciding true or false value for a given value of x fine. So as a core concept
of linear regression, you can say that the data
is modeled using a straight. But in the case
of logistic regression the data is module using
a sigmoid function. The linear regression is used
with continuous variables on the other hand
the logistic regression. It is used with categorical
variable the output or the prediction
of a linear regression is a value of the variable on the other hand
the output of production of a logistic regression
is the probability of occurrence of the event. Now, how will you
check the accuracy and goodness of fit in case
of linear regression? We are various methods
like measured by loss R square. Are adjusted r squared Etc while in the case
of logistic regression you have accuracy precision
recall F1 score, which is nothing but
the harmonic mean of precision and recall next is Roc curve for determining the probability
threshold for classification or the confusion Matrix Etc. There are many all right. So summarizing the difference between linear and
logistic regression. You can say that the type of function you
are mapping to is the main point of difference between linear
and logistic regression a linear regression model. The Continuous X2 a continuous
file on the other hand a logistic regression
Maps a continuous x to the bindery why so we can use logistic
regression to make category or true false decisions
from the data find so let's move
on ahead next is linear regression selection criteria, or you can say when will
you use linear regression? So the first is classification and regression capabilities
regression models predict a continuous variable such as
the sales made on a day or predict the temperature of a city T their Reliance
on a polynomial like a straight line
to fit a data set poses a real challenge when it comes towards building
a classification capability. Let's imagine that you fit
a line with a train points that you have now imagine you
add some more data points to it. But in order to fit it,
what do you have to do? You have to change
your existing model that is maybe you have
to change the threshold itself. So this will happen
with each new data point you are to the model hence. The linear regression is not
good for classification models. Fine. Next is data quality. Each missing value
removes one data point that could optimize the regression and
simple linear regression. The outliers can significantly disrupt the outcome
just for now. You can know that if you
remove the outliers your model will become very good. All right. So this is about data quality. Next is computational complexity
a linear regression is often not computationally expensive as
compared to the decision tree or the clustering algorithm
the order of complexity for n training example
and X features usually Falls in either Big O of x Or bigger of xn
next is comprehensible and transparent the
linear regression are easily comprehensible
and transparent in nature. They can be represented by
a simple mathematical notation to anyone and can be
understood very easily. So these are some
of the criteria based on which you will select
the linear regression algorithm. All right. Next is where is linear
regression used first is evaluating trans
and sales estimate. Well linear regression
can be used in business to evaluate Trends
and make estimates. Forecast for example, if a company sales have
increased steadily every month for past few years then
conducting a linear analysis on the sales data
with monthly sales on the y axis and time on the x axis. This will give you a line that predicts the upward Trends
in the sale after creating the trendline the company
could use the slope of the lines to focused
sale in future months. Next is analyzing. The impact of price changes
will linear regression can be used to analyze
the effect of pricing on Omer behavior for instance, if a company changes the price on a certain
product several times, then it can record the quantity
itself for each price level and then perform
a linear regression with sold quantity as a dependent variable and price
as the independent variable. This would result in a line
that depicts the extent to which the customer reduce
their consumption of the product as the prices increasing. So this result would help us
in future pricing decisions. Next is assessment of risk
in financial services and insurance domain. Linear regression can be used
to analyze the risk, for example health insurance
company might conduct a linear regression algorithm how it can do it can do it
by plotting the number of claims per customer against its age
and they might discover that the old customers tend to make more
health insurance claim. Well the result
of such analysis might guide important business decisions. All right, so by now you
have just a rough idea of what linear regression
algorithm as like what it does where it is used
when You should use it early. Now. Let's move on
and understand the algorithm and depth so suppose
you have independent variable on the x-axis and dependent
variable on the y-axis. All right suppose. This is the data point
on the x axis. The independent variable
is increasing on the x-axis. And so does the dependent
variable on the y-axis? So what kind of linear
regression line you would get you would get a positive
linear regression line. All right as the slope would
be positive next is suppose. You have an independent
variable on the X axis which is increasing and on the other hand the
dependent variable on the y-axis that is decreasing. So what kind of line
will you get in that case? You will get
a negative regression line. In this case as the slope
of the line is negative and this particular line that is line of y equal MX plus C is a line
of linear regression which shows the relationship
between independent variable and dependent variable and this line is only known
as line of linear regression. Okay. So let's add
some data points, too. Our graph so these
are some observation or data points on our graph. So let's plot some more. Okay. Now all our data points
are plotted now our task is to create a regression line
or the best fit line. All right now once our regression
line is drawn now, it's the task
of production now suppose. This is our estimated value
or the predicted value and this is our actual value. Okay. So what we have to do our main
goal is to reduce this error that is to reduce the distance between the Estimated
or the predicted value and the actual value the best
fit line would be the one which had the least error or the least difference
in estimated value and the actual value. All right, and other words we
have to minimize the error. This was a brief understanding of linear regression
algorithm soon. We'll jump towards
mathematical implementation. But for then let me tell you
this suppose you draw a graph with speed on the x-axis and distance covered on the y axis with
the time domain in constant. If you plot a graph
between the speed travel by the vehicle and the distance traveled
in a fixed unit of time, then you will get
a positive relationship. All right. So suppose the equation
of a line is y equal MX plus C. Then in this case Y is
the distance traveled in a fixed duration of time x is the speed of vehicle m
is the positive slope of the line and see is
the y-intercept of the line. All right suppose
the distance remaining constant. You have to plot a graph
between the speed of the vehicle and the time taken
to travel a fixed distance. Then in that case
you will get a line with a negative relationship. All right, the slope of the line
is negative here the equation of line changes to y
equal minus of MX plus C where Y is the time
taken to travel a fixed distance X is the speed of vehicle m is
the negative slope of the line and see is
the y-intercept of the line. All right. Now, let's get back to our independent
and dependent variable. So in that term,
why is our dependent variable and X that is
our independent variable now, let's move on. And see them at the magical
implementation of the things. Alright, so we have x equal 1 2 3 4 5 let's plot
them on the x-axis. So 0 1 2 3 4 5 6 align
and we have y as 3 4 2 4 5. All right. So let's plot 1 2 3 4 5 on the y-axis now, let's plot our coordinates 1
by 1 so x equal 1 and y equal 3, so we have here x equal 1 and y equal 3 So this is the point
1 comma 3 so similarly we have 1 3 2 4 3 2 4 4 & 5 5. Alright, so moving on ahead. Let's calculate the mean of X
and Y and plot it on the graph. All right, so mean of X is 1 plus 2 plus 3 plus 4
plus 5 divided by 5. That is 3. All right, similarly mean
of Y is 3 plus 4 plus 2 plus 4 plus 5 that is 18. So we 10 divided by 5. That is nothing but 3.6. Alright, so next
what we'll do we'll plot. I mean that is 3 comma
3 .6 on the graph. Okay. So there's a point 3 comma 3 .6 see our goal is to find
or predict the best fit line using the least Square
Method All right. So in order to find that we first need to find
the equation of line, so let's find the equation
of our regression line. Alright, so let's suppose
this is our regression line y equal MX plus C. Now. We have an equation of line. So all we need to do
is find the value of M and C. I wear m equals
summation of x minus X bar X Y minus y bar
upon the summation of x minus X bar whole Square
don't get confused. Let me resolve it for you. All right. So moving on ahead
as a part of formula. What we are going to do
will calculate x minus X bar. So we have X as 1 minus X bar
as 3 so 1 minus 3 that is minus 2 next. We have x equal
to minus its mean 3 that is minus 1
similarly we 3 - 3 0 4 minus 3 1 5 - 3 2. All right, so x minus X bar. It's nothing but the distance
of all the point through the line y equal 3 and what does this y minus y bar implies
it implies the distance of all the point from the line x equal 3 .6 fine. So let's calculate the value
of y minus y bar. So starting with y equal 3 - value of y bar
that is 3.6. So it is three minus three. .6. How much - of 0.6 next is 4 minus 3.6
that is 0.4 next to minus 3.6 that is - of 1.6. Next is 4 minus 3.6
that is 0.4 again, 5 minus 3.6 that is 1.4. Alright, so now we are done
with Y minus y bar fine now next we will calculate x
minus X bar whole Square. So let's calculate x
minus X bar whole Square so it is - 2 whole square that is
4 minus 1 whole square. That is 1 0 squared is
0 1 Square 1 2 square for fine. So now in our table we have x
minus X bar y minus y bar and x minus X bar whole Square. Now what we need. We need the product of x
minus X bar X Y minus y bar. Alright, so let's see
the product of x minus X bar X Y minus y bar that is minus
of 2 x minus of 0.6. That is 1.2 minus
of 1 x 0 point 4. That is minus. - of zero point 4 0 x
minus of 1.6. That is 0 1 multiplied
by zero point four that is 0.4. And next 2 multiplied
by 1 point for that is 2.8. All right. Now almost all the parts
of our formula is done. So now what we need
to do is get the summation of last two columns. All right, so the summation of x
minus X bar whole square is 10 and the summation of x minus X bar X Y minus y bar is for So the value of M
will be equal to 4 by 10 fine. So let's put this value of m equals zero point 4
and our line y equal MX plus C. So let's file all the points
into the equation and find the value of C. So we have y as 3.6 remember
the mean by m as 0.4 which we calculated just
now X as the mean value of x that is 3 and we have the equation as
3 point 6 equals 0 .4 Applied by 3 plus C. Alright that is 3.6 equal
1 Point 2 plus C. So what is the value of C
that is 3.6 minus 1.2. That is 2.4. All right. So what we had we had m equals
zero point four C as 2.4. And then finally when we calculate the equation
of the regression line, what we get is y equal
zero point four times of X plus two point four. So this is the regression line. All right, so there is how you are plotting
your points this Actual point. All right now for given m equals
zero point four and SQL 2.4. Let's predict the value of y
for x equal 1 2 3 4 & 5. So when x equal
1 the predicted value of y will be zero point four x one plus two point
four that is 2.8. Similarly when x equal
to predicted value of y will be zero point 4 x 2 + 2 point 4 that equals
to 3 point 2 similarly x equal 3 y will be 3. .6. X equals 4 y will be 4 point 0 x equal 5 y will be
four point four. So let's plot them on the graph and the line passing through
all these predicting point and cutting y-axis at 2.4
as the line of regression. Now your task is to calculate
the distance between the actual and the predicted value and your job is
to reduce the distance. All right, or in other words, you have to reduce the error
between the actual and the predicted value the line with the least error will be
the line of linear regression. Chicken or regression line and it will also be
the best fit line. All right. So this is how things
work in computer. So what it do it performs
n number of iteration for different values of M
for different values of M. It will calculate
the equation of line where y equals MX plus C. Right? So as the value
of M changes the line is changing so iteration
will start from one. All right, and it will perform
a number of iteration. So after every iteration what it will do it
will calculate the predicted. Value according to the line
and compare the distance of actual value
to the predicted value and the value of M for which the distance
between the actual and the predicted value is
minimum will be selected as the best fit line. All right. Now that we have calculated
the best fit line now, it's time to check the goodness
of fit or to check how good a model is performing. So in order to do that, we have a method
called R square method. So what is this R square? Well r-squared value is
a statistical measure of how close the data are to the fitted regression
line in general. It is considered that a high r-squared
value model is a good model, but you can also have
a lower squared value for a good model as well or a higher squared
value for a model that does not fit at all. I like it is also known as
coefficient of determination or the coefficient
of multiple determination. Let's move on and see
how a square is calculated. So these are our actual values
plotted on the graph. We had calculated
the predicted values of Y as 2.8 3.2 3.6 4.0 4.4. Remember when we calculated
the predicted values of Y for the equation Y
predicted equals 0 1 4 x of X plus two point
four for every x equal 1 2 3 4 & 5 from there. We got the Ed values
of Phi all right. So let's plot it on the graph. So these are point
and the line passing through these points are nothing
but the regression line. All right. Now what you need to do is you have to check and compare
the distance of actual - mean versus the distance
of predicted - mean alike. So basically what you are doing
you are calculating the distance of actual value to the mean
to distance of predicted value to the mean I like so there is nothing but a square in mathematically
you can represent our school. Whereas summation of Y
predicted values minus y bar whole Square divided
by summation of Y minus y bar whole Square where Y is the actual value
y p is the predicted value and Y Bar is the mean value of y
that is nothing but 3.6. Remember, this is our formula. So next what we'll do
we'll calculate y minus. Y1. So we have y is
3y bar as 3 point 6, so we'll calculate
it as 3 minus 3.6 that is nothing but minus of 0.6
similarly for y equal 4 and Y Bar equal 3.6. We have y minus y bar as
zero point 4 then 2 minus 3.6. It is 1 point 6 4 minus
3.6 again zero point four and five minus 3.6 it is 1.4. So we got the value
of y minus y bar. Now what we have to do we
have to take it Square. So we have minus 0.6 Square
as 0.36 0.4 Square as 0.16 - of 1.6 Square as 2.56 0.4 Square
as 0.16 and 1.4 squared is 1.96 now is a part
of formula what we need. We need our YP
minus y BAR value. So these are VIP values and we
have to subtract it from the No, why so 2 .8 minus 3.6
that is minus 0.8. Similarly. We will get 3.2 minus 3.6
that is 0.4 and 3.6 minus 3.6. That is 0 for 1 0 minus
3.6 that is 0.4. Then 4 .4 minus 3.6 that is 0.8. So we calculated the value
of YP minus y bar now, it's our turn to calculate
the value of y b minus y bar whole Square next. We have - of 0.8 Square as 0.64 - of Point
four square as 0.160 Square 0 0 point 4 Square as again 0.16
and 0.8 Square as 0.64. All right. Now as a part of formula what it suggests it suggests
me to take the summation of Y P minus y bar whole square and summation of Y minus
y bar whole Square. All right. Let's see. So in submitting y
minus y bar whole Square what you get is five point two
and summation of Y P minus y bar whole Square you
get one point six. So the value of R square
can be calculated as 1 point 6 upon 5.2 fine. So the result which will get
is approximately equal to 0.3. Well, this is not a good fit. All right, so it suggests that the data points are far
away from the regression line. Alright, so this is how your graph will look
like when R square is 0.3 when you increase the value
of R square to 0.7. So you'll see that the actual value would like
closer to the regression line when it reaches to 0.9 it comes. More clothes and when the value
of approximately equals to 1 then the actual values lies
on the regression line itself, for example, in this case. If you get a very low value
of R square suppose 0.02. So in that case what will see that the actual values
are very far away from the regression line
or you can say that there are too
many outliers in your data. You cannot focus
and thing from the data. All right. So this was all about the
calculation of our Square now, you might get a question
like are low values of Square always bad. Well in some field it
is entirely expected that I ask where value will be low. For example any field that attempts to predict human
behavior such as psychology typically has r-squared values
lower than around 50% through which you can conclude that humans are simply harder to predict the under
physical process furthermore. If you ask what value is low, but you have statistically
significant predictors, then you can still
draw important conclusion about how changes in the
predicator values associated. Created with the changes
in the response value regardless of the r-squared the significant coefficient
still represent the mean change in the response for one unit
of change in the predicator while holding other predicated
in the model constant. Obviously this type of information can be
extremely valuable. All right. All right. So this was all about
the theoretical concept now, let's move on to the coding part and understand the
code in depth. So for implementing
linear regression using python, I will be using Anaconda with jupyter notebook
installed on it. So I like there's
a jupyter notebook and we are using
python 3.0 on it. Alright, so we are going
to use a data set consisting of head size and human brain
of different people. All right. So let's import our data set
percent matplotlib and line. We are importing numpy as NP pandas as speedy and
matplotlib and from matplotlib. We are importing pipe lot
of that as PLT. Alright next we will import
our data had brain dot CSV and store it
in the database table. Let's execute the Run button
and see the armor. But so this task
symbol it symbolizes that it still executing. So there's a output
our data set consists of two thirty seven rows
and 4 columns. We have columns as
gender age range head size in centimeter Cube and brain weights
and Graham fine. So there's our sample data set. This is how it looks it consists
of all these data set. So now that we
have imported our data, so as you can see they are
237 values in the training set so we can find a linear. Relationship between the head
size and the Brain weights. So now what we'll do
we'll collect X & Y the X would consist
of the head size values and the Y would consist
of brain with values. So collecting X and Y.
Let's execute the Run. Done next what we'll do we
need to find the values of b 1 or B not or you can say m and C. So we'll need the mean of X
and Y values first of all what we'll do we'll calculate
the mean of X and Y so mean x equal NP dot Min X. So mean is a predefined function
of Numb by similarly mean underscore y equal
NP dot mean of Y, so what it will return if you'll return
the mean values of Y next we'll check
the total number of values. So m equals. Well length of X. Alright, then we'll use the formula
to calculate the values of b 1 and B naught or MNC. All right, let's execute
the Run button and see what is the result. So as you can see
here on the screen, we have got d 1 as 0 point 2 6 3 and be not as three twenty
five point five seven. Alright, so now
that we have a coefficient. So comparing it with
the equation y equal MX plus C. You can say
that brain weight equals zero point 2 6 3 X Head size
plus three twenty five point five seven so you can say that the value of M
here is zero point 2 6 3 and the value of C. Here is three twenty
five point five seven. All right, so there's
our linear model now, let's plot it
and see graphically. Let's execute it. So this is how our plot looks
like this model is not so bad. But we need to find out
how good our model has. So in order to find
it the many methods like root mean Square method
the coefficient of determination or the a square method. So in this tutorial, I have told you
about our score method. So let's focus on that and see
how good our model is. So let's calculate
the R square value. All right here SS underscore T
is the total sum of square SS. I is the total sum of square
of residuals and R square as the formula is
1 minus total sum of squares upon total sum
of square of the residuals. All right next
when you execute it, you will get the value
of R square as 0.63 which is pretty very good. Now that you have implemented
simple linear regression model using least Square method, let's move on and see how will you implement the model
using machine learning library called scikit-learn. All right. So this scikit-learn
is a simple machine. Owning library in Python welding
machine learning model are very easy using scikit-learn. So suppose there's
a python code. So using the scikit-learn
libraries your code shortens to this length like so let's execute the Run button and see you
will get the same our to score. So today we'll be discussing
logistic regression. So let's move forward and understand the what and by
of logistic regression. Now this algorithm
is most widely used when the dependent variable or you can see the output is
in the binary format. And so here you need
to predict the outcome of a categorical
dependent variable. So the outcome should be
always discreet or categorical in nature Now by discrete. I mean the value
should be binary or you can say you just have
two values it can either be 0 or 1 you can either be yes or a no either be true
or false or high or low. So only these can be
the outcomes so the value which you need to protect
should be discrete or you can say
categorical in nature. Whereas in linear regression. We have the value of by
or you can say the value. Two predictors in a Range that is how there's a difference
between linear regression and logistic regression. We must be having question. Why not linear regression now
guys in linear regression the value of buyer or the value which you need
to predict is in a range, but in our case as
in the logistic regression, we just have two values
it can be either 0 or it can be one. It should not entertain
the values which is below zero or above one. But in linear regression, we have the value of y
in the range so here in order to implement logic regression. We need to clip this This part
so we don't need the value that is below zero
or we don't need the value which is above 1 so since the value of y will be
between only 0 and 1 that is the main rule
of logistic regression. The linear line has to be
clipped at zero and one now. Once we clip this graph it
would look somewhat like this. So here you are
getting the curve which is nothing but
three different straight lines. So here we need to make
a new way to solve this problem. So this has to be
formulated into equation and hence we come up
with logistic regression. So here the outcome
is either 0 or 1. Which is the main rule
of logistic regression. So with this our resulting curve
cannot be formulated. So hence our main aim
to bring the values to 0 and 1 is fulfilled. So that is how we came up with
large stick regression now here once it gets formulated
into an equation. It looks somewhat like this. So guys, this is
nothing but an S curve or you can say the sigmoid curve
a sigmoid function curve. So this sigmoid function
basically converts any value from minus infinity to Infinity
pure discrete values, which a Logitech regression
wants or you can say the Values which are in binary
format either 0 or 1. So if you see here
the values and either 0 or 1 and this is nothing
but just a transition of it, but guys there's
a catch over here. So let's say I have
a data point that is 0.8. Now, how can you decide whether your value is 0 or 1 now here you
have the concept of threshold which basically
divides your line. So here threshold value basically indicates the
probability of either winning or losing so here by winning. I mean the value is equals to 1. Am I losing I mean
the values equal to 0 but how does it do that? Let's have a data point
which is over here. Let's say my cursor is at 0.8. So here I check whether this value is less
than the threshold value or not. Let's say if it is more
than the threshold value. It should give me the result
as 1 if it is less than that, then should give me
the result is zero. So here my threshold
value is 0.5. I need to Define that
if my value let's is 0.8. It is a more than 0.5. Then the value should
be rounded of to 1. Let's see if it is
less than 0.5. Let's I have a value 0.2 then
should reduce it to zero. So here you can use the concept of threshold value
to find output. So here it should be discreet. It should be either 0
or it should be one. So I hope you caught this curve
of logistic regression. So guys, this is
the sigmoid S curve. So to make this curve
we need to make an equation. So let me address
that part as well. So let's see how an equation
is formed to imitate this functionality so over here, we have an equation
of a straight line. It is y is equal to MX plus C. So in this case, I just have only one independent
variable but let's say if we have many
independent variable then the equation becomes m 1 x
1 plus m 2 x 2 plus m 3 x 3 and so on till M NX n now, let us put in B and X. So here the equation
becomes Y is equal to b 1 x 1 plus beta 2 x 2 plus b 3 x 3 and so on
till be nxn plus C. So guys the equation of the straight line has a range
from minus infinity to Infinity. But in our case or you can say
largest equation the value which we need to predict
or you can say the Y value it can have the range
only from 0 to 1. So in that case we need
to transform this equation. So to do that what we had done we have just divide
the equation by 1 minus y so now Y is equal to 0 so 0 over 1 minus 0
which is equal to 1 so 0 over 1 is again 0 and if you take Y is equals
to 1 then 1 over 1 minus 1 which is 0 so 1 over 0 is infinity. So here my range is now
between You know to Infinity, but again, we want the range
from minus infinity to Infinity. So for that what we'll do we'll have
the log of this equation. So let's go ahead
and have the logarithmic of this equation. So here we have this transform
it further to get the range between minus infinity
to Infinity so over here we have log of Y over 1 minus 1 and this is your final
logistic regression equation. So guys, don't worry. You don't have to write
this formula or memorize this formula in Python. You just need to
call this function which is logistic regression and everything will be be
automatically for you. So I don't want to scare
you with the maths in the formulas behind it, which is always good to know
how this formula was generated. So I hope you guys are clear with how logistic regression
comes into the picture next. Let us see what are
the major differences between linear regression was
a logistic regression the first of all in linear regression, we have the value of y as a continuous variable
or the variable between need to predict
are continuous in nature. Whereas in logistic regression. We have the categorical variable
so here the value which you need to predict
should be Creating nature. It should be either 0 or 1 or should have
just two values to it. For example, whether it is raining
or it is not raining is it humid outside
or it is not humid outside. Now, does it going to snow
and it's not going to snow? So these are the few example, we need to predict where the values are discrete
or you can just predict whether this is
happening or not. Next linear equation solves
your regression problems. So here you have a concept
of independent variable and the dependent variable. So here you can calculate
the value of y which you need to predict
using the A of X so here your y variable
or you can see the value that you need to
predict are in a range. But whereas in
logistic regression you have discrete values. So logistic regression basically
solves a classification problem so it can basically classify it
and it can just give you result whether this event
is happening or not. So I hope it is pretty much Clear till now
next in linear equation. The graph that you have seen is a straight line graph
so over here, you can calculate the value
of y with respect to the value of x where as in logistic
regression because of that. The got was a Escobar you
can see the sigmoid curve. So using the sigmoid function
You can predict your y-values moving the I let
us see the various use cases where in logistic regression
is implemented in real life. So the very first is
weather prediction now largest aggression helps
you to predict your weather. For example, it
is used to predict whether it is raining
or not whether it is sunny. Is it cloudy or not? So all these things
can be predicted using logistic regression. Where as you need
to keep in mind that both linear regression. And logistic regression can be
used in predicting the weather. So in that case linear equation
helps you to predict what will be
the temperature tomorrow whereas logistic regression
will only tell you which is going to rain or not
or whether it's cloudy or not, which is going to snow or not. So these values are discrete. Whereas if you apply
linear regression you the predicting things like what
is the temperature tomorrow or what is the temperature
day after tomorrow and all those thing? So these are
the slight differences between linear regression and logistic regression
the moving ahead. We have classification problem. Sighs on performs
multi-class classification. So here it can help you tell
whether it's a bird. It's not a bird. Then you classify
different kind of mammals. Let's say whether it's a dog
or it's not a dog similarly. You can check it for reptile whether it's a reptile
or not a reptile. So in logistic regression, it can perform
multi-class classification. So this point
I've already discussed that it is used
in classification problems next. It also helps you to determine
the illness as well. So let me take an example. Let's say a patient goes for
a routine check up in hospital. So what doctor will do it, it will perform various tests
on the patient and will check whether the patient is
actually l or not. So what will be the features so doctor can check
the sugar level the blood pressure then what
is the age of the patient? Is it very small or is
it old person then? What is the previous medical
history of the patient and all of these features
will be recorded by the doctor and finally doctor checks
the patient data and Data - the outcome of an illness
and the severity of illness. So using all the data
a doctor can identify with A patient is ill or not. So these are
the various use cases in which you can use
logistic regression now, I guess enough of theory part. So let's move ahead and see some
of the Practical implementation of logistic regression
so over here, I be implementing two projects when I have the data set of Titanic so over here
will predict what factors made people more likely to survive
the sinking of the Titanic ship and my second project
will see the data analysis on the SUV cars so over here we
have the data of the SUV cars who can purchase it. And what factors made people
more interested in buying SUV? So these will be
the major questions as to why you should Implement logistic regression and
what output will you get by it? So let's start by
the very first project that is Titanic data analysis. So some of you might know that there was a ship
called as Titanic with basically hit an iceberg and it sunk to the bottom
of the ocean and it was a big disaster at that time because it was the first
voyage of the ship and it was supposed to be really
really strongly built and one of the best ships of that time. So it was a big Disaster of that time and of course there
is a movie about this as well. So many of you
might have washed it. So what we have we have data
of the passengers those who survived and those who did not survive
in this particular tragedy. So what you have to do you
have to look at this data and analyze which factors
would have been contributed the most to the chances of a person survival
on the ship or not. So using the logistic
regression, we can predict whether the person survived or the person died
now apart from this. We also have a look
with the various features along with that. So first, let us explore
The data set so over here. We have the index value
then the First Column is passenger ID. Then my next column is survived. So over here, we have two values
a 0 and a 1 so 0 stands for did not survive
and one stands for survive. So this column is categorical where the values
are discrete next. We have passenger class
so over here, we have three values 1 2 and 3. So this basically tells you that whether a passengers travelling
in the first class second class or third class, then we have the name of the We have the six or you can see
the gender of the passenger where the passenger
is a male or female. Then we have the age
we had sip SP. So this basically means
the number of siblings or the spouses aboard
the Titanic so over here, we have values such as 1
0 and so on then we have Parts apart is basically
the number of parents or children aboard
the Titanic so over here, we also have some values then we have the ticket number. We have the fair. We have the table number
and we have the embarked column. So in my inbox column, we have three values
we have SC and Q. So as basically stands for Southampton C
stands for Cherbourg and Q stands for Cubans down. So these are the features that will be applying
our model on so here we'll perform various steps and then we'll be implementing
logistic regression. So now these are
the various steps which are required
to implement any algorithm. So now in our case
we are implementing logistic regression soft. Very first step is
to collect your data or to import the libraries that are used for
collecting your data. And then taking it forward then
my second step is to analyze your data so over here I can go
to the various fields and then I can analyze the data. I can check that the females or children survive
better than the males or did the rich
passenger survived more than the poor passenger
or did the money matter as in who paid mode to get
into the ship with the evacuated first? And what about the workers
does the worker survived or what is the survival rate if you were the worker
in the ship and not just a traveling passenger? So all of these are
very very and questions and you would be going
through all of them one by one. So in this stage, you need to analyze our data and explore your data as much
as you can then my third step is to Wrangle your data now data wrangling basically means
cleaning your data so over here, you can simply remove
the unnecessary items or if you have a null values
in the data set. You can just clear that data and
then you can take it forward. So in this step you
can build your model using the train data set and then you can test it using a test so over here you
will be performing a split which basically Get
your data set into training and testing data set and find
you will check the accuracy. So as to ensure how much accurate
your values are. So I hope you guys got
these five steps that you're going to implement
in logistic regression. So now let's go into all
these steps in detail. So number one. We have to collect your data or you can say
import the libraries. So it may show you
the implementation part as well. So I just open
my jupyter notebook and I just Implement all
of these steps side by side. So guys this is
my jupyter notebook. So first, let me just rename
jupyter notebook to let's say Titanic data analysis. Now a full step was
to import all the libraries and collect the data. So let me just import
all the library's first. So first of all,
I'll import pandas. So pandas is used
for data analysis. So I'll say import pandas as PD
then I will be importing numpy. So I'll say import numpy as NP
so number is a library in Python which basically stands
for numerical Python and it is widely used to perform
any scientific computation. Next. We will be importing Seaborn. So c 1 is a library for statistical plotting so
Say import Seaborn as SNS. I'll also import matplotlib. So matplotlib library
is again for plotting. So I'll say import
matplotlib dot Pi plot as PLT now to run this library
in jupyter Notebook all I have to write in his percentage
matplotlib in line. Next I will be importing
one module as well. So as to calculate the basic
mathematical functions, so I'll say import maths. So these are the libraries that I will be needing
in this Titanic data analysis. So now let me just
import my data set. So I'll take a variable. Let's say Titanic data
and using the pandas. I will just read my CSV
or you can see the data set. I like the name of my data set
that is Titanic dot CSV. Now. I have already showed you
the data set so over here. Let me just bring
the top 10 rows. So for that I will just say I take the variable
Titanic data dot head and I'll say the top ten rules. So now I'll just run this so to run this style is have
to press shift + enter or else you can just directly
click on this cell so over here. I have the index. We have the passenger ID,
which is nothing. But again the index which is starting from 1 then
we have the survived column which has a category. Call values or you can say
the discrete values, which is in the form of 0 or 1. Then we have
the passenger class. We have the name of
the passenger sex age and so on. So this is the data set that I will be going forward with next let us print
the number of passengers which are there in this original
data frame for that. I'll just simply type in print. I'll say a number of passengers. And using the length function, I can calculate
the total length. So I'll say length and inside this I'll be
passing this variable because Titanic data,
so I'll just copy it from here. I'll just paste it dot index and next set me
just bring this one. So here the number of passengers which are there in the original data set
we have is 891 so around this number would traveling in
the Titanic ship so over here, my first step is done where you have just collected
data imported all the libraries and find out the total
number of passengers, which are Titanic so
now let me just go back to presentation and let's see. What is my next step. So we're done with
the collecting data. Next step is to analyze
your data so over here will be creating different plots
to check the relationship between variables as in how one variable
is affecting the other so you can simply explore
your data set by making use of various columns and then you can plot
a graph between them. So you can either plot
a correlation graph. You can plot
a distribution curve. It's up to you guys. So let me just go back to my jupyter notebook and let
me analyze some of the data. Over here. My second part is
to analyze data. So I just put this in headed
to now to put this in here to I just have to go
on code click on mark down and I just run this so first let us plot account plot where you can pay
between the passengers who survived and
who did not survive. So for that I will be using
the Seabourn Library so over here I have imported
Seaborn as SNS so I don't have
to write the whole name. I'll simply say
SNS dot count plot. I say axis with the survive
and the data that I'll be using
is the Titanic data or you can say the name
of variable in which you have store your data set. So now let me just run this so who were here as you can see
I have survived column on my x axis and on the y axis. I have the count. So zero basically stands
for did not survive and one stands
for the passengers who did survive so over here, you can see that around 550
of the passengers who did not survive and they
were around 350 passengers who only survive so here
you can basically conclude. There are very less survivors
than on survivors. So this was the very first plot
now there is not another plot to compare the sex as to whether
out of all the passengers who survived and
who did not survive. How many were men and
how many were female so to do that? I'll simply say
SNS dot count plot. I add the Hue as six
so I want to know how many females and
how many male survive then I'll be
specifying the data. So I'm using Titanic data
set and let me just run this you have done a mistake over here so over here you can see I have survived
column on the x-axis and I have the count
on the why now. So have you color stands
for your male passengers and orange stands
for your female? So as you can see
here the passengers who did not survive that has a value
0 so we can see that. Majority of males did not
survive and if we see the people who survived here, we can see the majority
of female survive. So this basically concludes
the gender of the survival rate. So it appears on average
women were more than three times more likely
to survive than men next. Let us plot another plot where we have the Hue as
the passenger class so over here we can see which class at
the passenger was traveling in whether it was traveling
in class 1 2 or 3. So for that I just
arrived the same command. I will say as soon as.com plot. I gave my x-axis as a family. I'll change my Hue
to passenger class. So my variable
named as PE class. And the data said that I'll be using
this Titanic data. So this is my result so over here you can see I have
blue for first-class orange for second class and green
for the third class. So here the passengers who did not survive a majorly
of the third class or you can say the lowest class or the cheapest class to get
into the dynamic and the people who did survive majorly belong
to the higher classes. So here 1 & 2 has more eyes
than the passenger who were traveling
in the third class. So here we have computed
that the passengers who did not survive
a majorly of third. Or you can see the lowest class and the passengers
who were traveling in first and second class
would tend to survive mode next. I just got a graph for
the age distribution over here. I can simply use my data. So we'll be using
pandas library for this. I will declare an array
and I'll pass in the column. That is H. So I plot and I
want a histogram. So I'll see plot da test. So you can notice over here that we have more
of young passengers, or you can see the children
between the ages 0 to 10 and then we have
the average people and if you go ahead Lester
would be the population. So this is the analysis
on the age column. So we saw that we have more young passengers and
more video courage passengers which are traveling
in the Titanic. So next let me plot
a graph of fare as well. So I'll say Titanic data. I say fair and again, I've got a histogram
so I'll say hissed. So here you can see
the fair size is between zero to hundred now. Let me add the bin size. So as to make it
more clear over here, I'll say Ben is equals to let's say 20 and I'll increase
the figure size as well. So I'll say fixed size. Let's say I'll give
the dimensions as 10 by 5. So it is bins. So this is more clear now next. It is analyzed
the other columns as well. So I'll just type
in Titanic data and I want the information as
to what all columns are left. So here we have passenger ID, which I guess it's
of no use then we have see how many passengers survived
and how many did not we also do the analysis
on the gender basis. We saw with a female
tend to survive more or the maintain to survive more
then we saw the passenger class where the passenger is traveling
in the first class second class or third class. Then we have the name. So in name,
we cannot do any analysis. We saw the sex we saw the ages. Well, then we have sea bass P. So this stands for the number
of siblings or the spouse is which Are aboard the Titanic so
let us do this as well. So I'll say SNS dot count plot. I mentioned X SC SP. And I will be using
the Titanic data so you can see the plot over here so over here
you can conclude that. It has the maximum value
on zero so we can conclude that neither children
nor a spouse was on board the Titanic now
second most highest value is 1 and then we have various values
for 2 3 4 and so on next if I go above the store
this column as well. Similarly can do four parts. So next we have part so you can see the number
of parents or children which are both the Titanic
so similarly can do. Israel then we have
the ticket number. So I don't think so. Any analysis is
required for Ticket. Then we have fears of a we
have already discussed as in the people would tend
to travel in the first class. You will pay the highest view
then we have the cable number and we have embarked. So these are the columns that will be doing
data wrangling on so we have analyzed the data and we have seen
quite a few graphs in which we can conclude which
variable is better than another or what is the relationship
the whole third step is my data wrangling
so data wrangling basically means Cleaning your data. So if you have a large data set, you might be having
some null values or you can say n values. So it's very important that you remove all
the unnecessary items that are present
in your data set. So removing this directly
affects your accuracy. So I just go ahead
and clean my data by removing all the Nan values
and unnecessary columns, which has a null value
in the data set the next time you're
performing data wrangling. Supposed to fall I'll check whether my dataset
is null or not. So I'll say Titanic data, which is the name of my data set
and I'll say is null. So this will basically tell
me what all values are null and will return me
a Boolean result. So this basically
checks the missing data and your result will be
in Boolean format as in the result will be true
or false so Falls mean if it is not null
and true means if it is null, so let me just run this. Over here you can see
the values as false or true. So Falls is where the value is
not null and true is where the value is none. So over here you can see
in the cabin column. We have the very first value which is null so we have to do
something on this so you can see that we have a large data set. So the counting does not stop and we can actually
see the some of it. We can actually print
the number of passengers who have the Nan value
in each column. So I say Titanic
underscore data is null and I want the sum of it. They've got some so this is
basically print the number of passengers who have the n
n values in each column so we can see that we have missing values
in each column that is 177. Then we have the maximum value
in the cave in column and we have very Less
in the Embark column. That is 2 so here if you don't want
to see this numbers, you can also plot a heat map and then you can visually
analyze it so let me just do that as well. So I'll say SNSD heat map. and say why tick labels
False child has run this as we have already seen that there were three columns in which missing data
value was present. So this might be age so over
here almost 20% of each column has a missing value then
we have the caping columns. So this is quite a large value and then we have two values
for embark column as well. Add a see map for color coding. So I'll say see map. So if I do this so the graph becomes
more attractive so over here yellow stands for Drew or you
can say the values are null. So here we have computed that we have the missing value
of H. We have a lot of missing values
in the cabin column and we have very less value, which is not even visible
in the Embark column as well. So to remove
these missing values, you can either replace
the values and you can put in some dummy values to it or you
can simply drop the column. So here let us suppose
pick the age column. So first, let me
just plot a box plot and they will analyze
with having a column as age so I'll say SNS dot box plot. I'll say x is equals
to passenger class. So it's PE class. I'll say Y is equal
to H and the data set that I'll be using
is Titanic side. So I'll say the data
is goes to Titanic data. You can see the edge in first class and second class
tends to be more older rather than we have it
in the third place. Well that depends
On the experience how much you earn on might be
there any number of reasons? So here we concluded that passengers who were traveling in class
one and class two a tend to be older than what we have
in the class 3 so we have found that we have some
missing values in EM. Now one way is to either just
drop the column or you can just simply fill
in some values to them. So this method is called
as imputation now to perform data wrangling or cleaning it is for spring
the head of the data set. So I'll say Titanic not head
so it's Titanic. For data, let's say I
just want the five rows. So here we have survived
which is again categorical. So in this particular column, I can apply
logic to progression. So this can be my y value
or the value that you need to predict. Then we have
the passenger class. We have the name then we
have ticket number Fair given so over here. We have seen that in keeping. We have a lot of null values
or you can say that any invalid which is quite visible as well. So first of all, we'll just drop this column
for dropping it. I'll just say
Titanic underscore data. And I'll simply type
in drop and the column which I need to drop so I
have to drop the cable column. I mention the access equals
to 1 and I'll say in place also to true. So now again, I just print the head
and a to see whether this column has been removed
from the data set or not. So I'll say Titanic dot head. So as you can see here, we don't have
given column anymore. Now, you can also
drop the na values. So I'll say Titanic data
dot drop all the any values or you can say Nan which is not a number and I will
say in place is equal to True. Let's Titanic. So over here, let me again plot the heat map
and let's say what the values which will be for showing
a lot of null values. Has it been removed or not. So I'll say SNSD heat map. I'll pass in the data set. I'll check it is null I say why
dick labels is equal to false. And I don't want color coding. So again I say false. So this will basically
help me to check whether my values
has been removed from the data set or not. So as you can see here,
I don't have any null values. So it's entirely black now. You can actually know
the some as well. So I'll just go above So
I'll just copy this part and I just use the sum function
to calculate the sum. So here the tells me that data set is green as
in the data set does not contain any null value or any n value. So now we have R Angela data. You can see cleaner data. So here we have done just
one step in data wrangling that is just removing
one column out of it. Now you can do a lot
of things you can actually fill in the values
with some other values or you can just
calculate the mean and then you can just fit
in the null values. But now if I see my data set, so I'll say
Titanic data dot head. But now if I see you over here I
have a lot of string values. So this has to be converted
to a categorical variables in order to implement
logistic regression. So what we will do
we will convert this to categorical variable into some dummy variables and
this can be done using pandas because logistic regression
just take two values. So whenever you apply machine
learning you need to make sure that there are
no string values present because it won't be taking
these as your input variables. So using string you don't have
to predict anything but in my case I have the survived
columns 2210 how many? People tend to survive and how men did not so 0 stands
for did not survive and one stands for survive. So now let me just
convert these variables into dummy variables. So I'll just use pandas
and I say PD not get dummies. You can simply press
tab to autocomplete and say Titanic data
and I'll pass the sex so you can just simply click
on shift + tab to get more information on this. So here we have
the type data frame and we have the passenger ID
survived and passenger class. So if Run this you'll see that 0 basically stands
for not a female and once and for it is a female similarly for
male zero Stanford's not made and one Stanford main now, we don't require
both these columns because one column
itself is enough to tell us whether it's male
or you can say female or not. So let's say if I want
to keep only mail I will say if the value of mail is 1 so it is definitely a maid
and is not a female. So that is how you don't need
both of these values. So for that I just
remove the First Column, let's say a female so
I'll say drop first. Andrew it has given
me just one column which is male and has
a value 0 and 1. Let me just set this as
a variable hsx so over here I can say sex dot head. I'll just want to see
the first pie Bros. Sorry, it's Dot. So this is how my data
looks like now here. We have done it for sex. Then we have
the numerical values in age. We have the numerical
values in spouses. Then we have the ticket number. We have the pair and we
have embarked as well. So in Embark,
the values are in SC and Q. So here also we can apply
this get dummy function. So let's say I
will take a variable. Let's say Embark. I'll use the pandas Library. I need the column name
that is embarked. Let me just print
the head of it. So I'll say Embark
dot head so over here. We have c q and s now here also
we can drop the First Column because these two
values are enough with the passenger
is either traveling for Q that is toonstone S4 sound time and if both the values
are 0 then definitely the passenger is from Cherbourg. That is the third value so you can again drop the first
value so I'll say drop. Let me just run this so this is how my output looks
like now similarly. You can do it for
passenger class as well. So here also we have
three classes one two, and three so I'll just
copy the whole statement. So let's say I want
the variable name. Let's say PCL. I'll pass in the column name that is PE class and I'll just
drop the First Column. So here also the values
will be 1 2 or 3 and I'll just remove
the First Column. So here we just left
with two and three so if both the values are 0 then
definitely the passengers traveling the first class now, we have made the values
as categorical now, my next step would be
to concatenate all these new rows into a data set. We can see Titanic data using
the pandas will just concatenate all these columns. So I'll say p Dot. One cat and then say
we have to concatenate sex. We have to concatenate
Embark and PCL and then I will mention
the access to one. I'll just run this can you to print the head so
over here you can see that these columns
have been added over here. So we have the mail column
with basically tells where the person is male or it's a female then
we have the Embark which is basically q and s so if it's traveling from Queenstown value
would be one else it would be 0 and If both
of these values are zeroed, it is definitely
traveling from Cherbourg. Then we have the passenger
class as 2 and 3. So the value of both these is 0 then passengers
travelling in class one. So I hope you got this
till now now these are the irrelevant columns that we have done over here so we can just drop
these columns will drop in PE class the embarked column
and the sex column. So I'll just type in Titanic data dot drop
and mention the columns that I want to drop. So I say And even lead
the passenger ID because it's nothing
but just the index value which is starting from one. So I'll drop this as well then
I don't want name as well. So I'll delete name as well. Then what else we can drop we
can drop the ticket as well. And then I'll just
mention the axis L say in place is equal to True. Okay, so the my column
name starts uppercase. So these has been dropped now, let me just bring
my data set again. So this is
my final leadership guys. We have the survived column which has the value zero and one
then we have the passenger class or we forgot to drop
this as well. So no worries. I'll drop this again. So now let me just run this. So over here we
have the survive. We have the H we
have the same SP. We have the parts. We have Fair mail and these
we have just converted. So here we have just
performed data angling for you can see clean the data and then we have just
converted the values of gender to male then embarked to qns and the passenger Class 2 2 & 3. So this was all
about my data wrangling or just cleaning the data then
my next up is training and testing your data. So here we will split
the data set into train subset and test steps. And then what we'll do
we'll build a model on the train data and then predict the output
on your test data set. So let me just go
back to Jupiter and it is implement
this as well over here. I need to train my data set. So I'll just put this
indeed heading 3. So over you need to Define
your dependent variable and independent variable. So here my Y is the output
for you can say the value that I need to predict
so over here, I will write Titanic data. I'll take the column
which is survive. So basically I have
to predict this column whether the passenger
survived or not. And as you can see we have
the discrete outcome, which is in the form of 0
and 1 and rest all the things we can take it as a features or you
can say independent variable. So I'll say Titanic data. Not a drop, so we just simply
drop the survive and all the other columns
will be my independent variable. So everything else are
the features which leads to the survival rate. So once we have defined
the independent variable and the dependent variable
next step is to split your data into training
and testing subset. So for that we will
be using SK loan. I just type in from sklearn
dot cross validation. import train display Now here if you just click
on shift and tab, you can go to the documentation and you can just see
the examples over here. And she can blast open it and then I just go
to examples and see how you can split your data. So over here you have
extra next test why drain why test and then using
the string test platelet and just passing your independent variable
and dependent variable and just Define a size
and a random straight to it. So, let me just copy this
and I'll just paste over here. Over here, we'll train test. Then we have the dependent
variable train and test and using the split function
will pass in the independent and dependent variable
and then we'll set a split size. So let's say I'll put it up 0.3. So this basically means that your data set
is divided in 0.3 that is in 70/30 ratio. And then I can add
any random straight to it. So let's say I'm applying
one this is not necessary. If you want the same result
as that of mine, you can add the random stream. So this would basically
take exactly the same sample every Next I have to train
and predict by creating a model. So here logistic
regression will graph from the linear regression. So next I'll just type in from SK loan dot linear model
import logistic regression. Next I'll just create the instance of this
logistic regression model. So I'll say log model is equals
to largest aggression now. I just need to fit my model. So I'll say log model dot fit and I'll just pass
in my ex train. And why it rain? It gives me all the details
of logistic regression. So here it gives me the class
made dual fit intercept and all those things then
what I need to do, I need to make prediction. So I will take a variable
insect addictions and I'll pass on the model to it. So I'll say
log model dot protect and I'll pass in the value
that is X test. So here we have just
created a model fit that model and then we
had made predictions. So now to evaluate how my model
has been performing. So you can simply
calculate the accuracy or you can also calculate
a classification report. So don't worry guys. I'll be showing both
of these methods. So I'll say from sklearn dot matrix
input classification report. Are you start fishing report? And inside this I'll be passing
in why test and the predictions? So guys this is
my classification report. So over here,
I have the Precision. I have the recall. We have the advanced code
and then we have support. So here we have the value
of decision as 75 72 and 73, which is not that bad now in order to calculate
the accuracy as well. You can also use the concept
of confusion Matrix. So if you want to print
the confusion Matrix, I will simply say from sklearn dot matrix import
confusion Matrix first of all, and then we'll just
print this So how am I function
has been imported successfully so is a confusion Matrix. And I'll again passing
the same variables which is why
test and predictions. So I hope you guys already know
the concept of confusion Matrix. So can you guys give me
a quick confirmation as to whether you guys remember this confusion
Matrix concept or not? So if not, I can just quickly
summarize this as well. Okay charged with you say so yes. Okay. So what is not clear with this? So I'll just tell
you in a brief what confusion Matrix is all about? So confusion Matrix is nothing
but a 2 by 2 Matrix which has a four outcomes
this basic tells us that how accurate
your values are. So here we have
the column as predicted. No predicted Y and we
have actual know an actual. Yes. So this is the concept
of confusion Matrix. So here let me just fade
in these values which we have just calculated. So here we have 105. 105 2125 and 63 So
as you can see here, we have got four outcomes now 105 is the value
where a model has predicted. No, and in reality. It was also a no so where we have predicted know
an actual know similarly. We have 63 as a predicted. Yes. So here the model predicted. Yes, and actually
also it was yes. So in order to
calculate the accuracy, you just need to add the sum of these two values and divide
the whole by the some. So here these two values
tells me where the order has. We predicted the correct output. So this value is also
called as true- This is called
as false positive. This is called as true positive and this is called
as false negative. Now in order to
calculate the accuracy. You don't have
to do it manually. So in Python, you can just import
accuracy score function and you can get
the results from that. So I'll just do that as well. So I'll say from sklearn
dot-matrix import accuracy score and I'll simply
print the accuracy. I'm passing the same variables. That is why I test
and predictions so over here. It tells me the accuracy as 78
which is quite good so over here if you want to do it manually we
have 2 plus these two numbers, which is 105 263. So this comes out to almost 168
and then you have to divide by the sum of all
the phone numbers. So 105 plus 63 plus 21 plus 25, so this gives me
a result of to 1/4. So now if you divide these two number you'll get
the same accuracy that is 98% or you can say .78. So that is how you
can calculate the accuracy. So now let me just go back
to my presentation and let's see what all we have
covered till now. So here we have First Data data
into train and test subset then we have build a model
on the train data and then predicted the output
on the test data set and then my fifth step
is to check the accuracy. So here we have calculator accuracy to almost
seventy eight percent, which is quite good. You cannot say
that accuracy is bad. So here tells me
how accurate your results. So him accuracy skoda finds that enhanced got
a good accuracy. So now moving ahead. Let us see the second project
that is SUV data analysis. So in this a car company has
released new SUV in the market and using the previous data
about the sales of their SUV. They want to predict
the category of people who might be interested
in buying this. So using the
logistic regression, you need to find what factors
made people more interested in buying this SUV. So for this let us hear data set
where I have user ID I have Of gender as male
and female then we have the age. We have the estimated salary and then we have
the purchased column. So this is my discreet column or you can see
the categorical column. So here we just have the value that is 0 and 1 and this column
we need to predict whether a person can actually
purchase a SUV or Not. So based on these factors,
we will be deciding whether a person can
actually purchase SUV or not. So we know the salary
of a person we know the age and using these we can predict whether person can
actually purchase SUV on Let me just go to my jupyter. Notebook and has implemented
a logistic regression. So guys, I will not be going
through all the details of data cleaning and analyzing
the part start part. I'll just leave it on you. So just go ahead
and practice as much as you can. Alright, so the second project
is SUV predictions. Alright, so first of all, I have to import
all the libraries so I say import numpy
SNP and similarly. I'll do the rest of it. Alright, so now let
me just bring the head of this data set. So this give already seen
that we have columns as user ID. We have gender. We have the age. We have the salary
and then we have to calculate whether person can actually
purchase a SUV or not. So now let us just simply go on
to the algorithm part. So we'll directly start off
with the logistic regression how you can train a model so
for doing all those things we first need to Define
an independent variable and a dependent variable. So in this case, I want my ex at is
an independent variable is a data set. I lock so here I will specify
sighing all the rows. So cool and basically stands
for that and in the columns, I want only two and
three dot values. So here we should fetch
me all the rows and only the second
and third column which is age and estimated salary. So these are the factors which will be used to predict
the dependent variable that is purchase. So here my dependent
variable is purchase any dependent variable is
of age and salary. So I'll say later said dot I log I'll have all the rows
and add just one for column. That is my position. Is column values. All right, so I just forgot when one square
bracket over here. Alright so over here. I have defined my independent
variable and dependent variable. So here my independent variable
is age and salary and dependent variable
is the column purchase. Now, you must be wondering
what is this? I lock function. So I look function is basically
an index of a panda's data frame and it is used
for integer based indexing or you can also say
selection by index now, let me just bring
these independent variables and dependent variable. So if I bring the independent
variable I have aged as well as a salary next. Let me print the dependent
variable as well. So over here you can see I
just have the values in 0 and 1 so 0 stands
for did not purchase next. Let me just divide my data set
into training and test subset. So I'll simply write in from SK loaned cross plate
dot cross validation. import rain test next I
just press shift and tab and over here. I will go to the examples
and just copy the same line. So I'll just copy this. I'll move the points now. I want to text size
to be let's see 25, so I have divided the trained
and tested in 75/25 ratio. Now, let's say I'll take
the random set of 0 So Random State basically
ensures the same result or you can say the same samples
taken whenever you run the code. So let me just run this now. You can also scale
your input values for better performing and this can be done
using standard scale. Oh, so let me do that as well. So I'll say
from sklearn pre-processing. Import standard scalar now. Why do we scale it now? If you see a data set we
are dealing with large numbers. Well, although we are using
a very small data set. So whenever you're working
in a prod environment, you'll be working
with large data set we will be using thousands and hundred thousands of do
people's so they're scaling down will definitely
affect the performance by a large extent. So here let me just show you how you can scale down
these input values and then the pre-processing contains all
your methods & functionality, which is required
to transform your data. So now let us scale down for tests as well as
their training data set. So else First Make
an instance of it. So I'll say standard scalar then I'll have Xtreme sasc dot
fit fit underscore transform. I'll pass in my Xtreme variable. And similarly I can do
it for test wherein I'll pass the X test. All right. Now my next step is
to import logistic regression. So I'll simply apply
logistically creation by first importing it so I'll say from sklearn sklearn the linear model import
logistic regression over here. I'll be using classifier. So is a classifier DOT is equals to largest aggression
so over here, I just make an instance of it. So I'll say logistic
regression and over here. I just pass in the random state, which is 0 No,
I simply fit the model. And I simply pass in
X train and white rain. So here it tells
me all the details of logistic regression. Then I have to
predict the value. So I'll say why I prayed
it's equals to classifier. Then predict function
and then I just pass in X test. So now we have
created the model. We have scale down
our input values. Then we have applied
logistic regression. We have predicted the values and now we want
to know the accuracy. So now the accuracy first we
need to import accuracy scores. So I'll say from
sklearn dot-matrix import actually see school and using this function we
can calculate the accuracy or you can manually do that by creating
a confusion Matrix. So I'll just pass. my lightest and my y
predicted All right. So over here I get
the accuracy is 89% So we want to know
the accuracy in percentage. So I just have to multiply it
by a hundred and if I run this so it gives me 89% So I hope you guys are clear with whatever I
have taught you today. So here I have taken
my independent variables as age and salary and then
we have calculated that how many people
can purchase the SUV and then we have calculated
our model by checking the accuracy so over here
we get the accuracy is 89 which is great. Alright guys that is
it for today. So I'll Discuss what we have covered
in today's training. First of all, we had a quick introduction
to what is regression and where their aggression
is actually use then we have understood
the types of regression and then got into the details of what and why
of logistic regression of compared linear was
in logistic regression. If you've also seen
the various use cases where you can Implement
logistic regression in real life and then we have picked
up two projects that is Titanic data analysis and SUV prediction so
over here we have seen how you can collect your data
analyze your data then perform. Modeling on that date
that train the data test the data and then finally
have calculated the accuracy. So in your SUV prediction, you can actually
analyze clean your data and you can do a lot of things so you can just go ahead
pick up any data set and explore it as
much as you can. What is classification. I hope every one of you
must have used Gmail. So how do you think the male
is getting classified as a Spam or not spam mail? Well, there's But
classification So What It Is Well
classification is the process of dividing the data set
into different categories or groups by adding label. In other way, you can say
that it is a technique of categorizing the observation
into different category. So basically what you
are doing is you are taking the data analyzing it and on the basis
of some condition you finely divided
into various categories. Now, why do we classify it? Well, we classify it
to perform predictive analysis on it like when you get the mail the machine
predicts it Be a Spam or not spam mail and on the basis
of that prediction it add the irrelevant or spam mail to the respective folder
in general this classification. Algorithm handled questions. Like is this data belongs
to a category or B category? Like is this a male or is this
a female something like that? I getting it? Okay fine. Now the question arises
where will you use it? Well, you can use this
of protection order to check whether the transaction
is genuine or not suppose. I am using a credit. Here in India now due to some reason I had
to fly to Dubai now. If I'm using the credit
card over there, I will get a notification alert
regarding my transaction. They would ask me to confirm
about the transaction. So this is also kind
of predictive analysis as the machine predicts that something fishy is in the transaction
as very for our ago. I made the transaction using
the same credit card and India and 24 hour later. The same credit card is being
used for the payment in Dubai. So the machine texts that
something fishy is going on in the transaction. So in order to confirm it it
sends you a notification alert. All right. Well, this is one of
the use case of classification you can even use it
to classify different items like fruits on the base
of its taste color size or weight a machine
well trained using the classification algorithm
can easily predict the class or the type of fruit whenever
new data is given to it. Not just the fruit. It can be any item. It can be a car. It can be a house. It can be a signboard. Or anything. Have you noticed that while you visit some sites or you try to login
into some you get a picture capture for that right where you have to identify whether the given image is of
a car or its of a pole or not? You have to select it
for example that 10 images and you're selecting
three Mages out of it. So in a way you are
training the machine, right you're telling that these three are
the picture of a car and rest are not so who knows you are training
at for something big right? So moving on ahead. Let's discuss the types
of education online. Well, there are
several different ways to perform the same tasks
like in order to predict whether a given person is a male or a female the machine
had to be trained first. All right, but there are multiple ways
to train the machine and you can choose any one of them just
for Predictive Analytics. There are many
different techniques, but the most common of them
all is the decision tree, which we'll cover in depth
in today's session. So it's a part
of classification algorithm. We have decision tree
random Forest name buys. K-nearest neighbor Lodge is Regression linear regression
support Vector machines and so on there are many. Alright, so let me give
you an idea about few of them starting
with decision tree. Well decision tree is
a graphical representation of all the possible solution to a decision the decisions which are made they
can be explained very easily. For example here is a task, which says that should I go
to a restaurant or should I buy a hamburger
you are confused on that. So for the artboard you
will do you will create a dish entry for it starting with the root node
will be first of all, you will check
whether you are hungry or not. All right, if you're not hungry then
just go back to sleep. Right? If you are hungry and you have $25 then you
will decide to go to restaurant and if you're hungry
and you don't have $25, then you will just
go and buy a hamburger. That's it. All right. So there's about decision tree
now moving on ahead. Let's see. What is a random Forest. Well random Forest build
multiple decision trees and merges them together
to get a more accurate and stable production. All right, most of the time
random Forest is trained with a bagging method. The bragging method
is based on the idea that the combination of learning module increases
the overall result. If you are combining the
learning from different models and then clubbing it together what it will do it will Increase
the overall result fine. Just one more thing. If the size of your
data set is huge. Then in that case one single
decision tree would lead to our Offutt model same way like a single person
might have its own perspective on the complete population as
a population is very huge. Right? However, if we implement
the voting system and ask different individual
to interpret the data, then we would be able
to cover the pattern in a much meticulous way
even from the diagram. You can see that in section A we have Howard large
training data set what we do. We first divide
our training data set into n sub-samples on it and we create a decision tree
for each cell sample. Now in the B part
what we do we take the vote out of every decision made
by every decision tree. And finally we Club
the vote to get the random Forest dition fine. Let's move on ahead. Next. We have neighbor Buys. So name bias is
a classification technique, which is based on Bayes theorem. It assumes that it's of any particular feature in
a class is completely unrelated to the presence of any other feature
named buys is simple and easy to implement algorithm
and due to a Simplicity this algorithm might out perform
more complex model when the size of the data set
is not large enough. All right, a classical use case of Navy bias is
a document classification. And that what you
do you determine whether a given text corresponds to one or more categories
in the Texas case, the features used might be
the presence or absence. Absence of any keyword. So this was about Nev
from the diagram. You can see
that using neighbor buys. We have to decide whether we have
a disease or not. First what we do we
check the probability of having a disease and not having the disease
right probability of having a disease is 0.1 while on the other hand
probability of not having a disease is 0.9. Okay first, let's see when we have disease
and we go to the doctor. All right, so when we
visited the doctor and the test is positive
Adjective so probability of having a positive test when you're having a disease
is 0.8 0 and probability of a negative test when you already have
a disease that is 0.20. This is also a false negative
statement as the test is detecting negative, but you still have
the disease, right? So it's a false
negative statement. Now, let's move ahead when you don't have
the disease at all. So probability of not having
a disease is 0.9. And when you visit the doctor
and the doctor is like, yes, you have the disease. But you already know
that you don't have the disease. So it's a false
positive statement. So probability of having
a disease when you actually know there is no disease
is 0.1 and probability of not having a disease when you actually know
there is no disease. So and the probability
of it is around 0.90 fine. It is same as probability
of not having a disease even the test is showing
the same results a true positive statement. So it is 0.9. All right. So let's move on ahead and
discuss about kn n algorithm. So this KNN algorithm
or the k-nearest neighbor, it stores all
the available cases and classifies new cases based
on the similarity measure the K in the KNN algorithm as
the nearest neighbor, we wish to take vote
from for example, if k equal 1 then the object
is simply assigned to the class of that single nearest neighbor
from the diagram. You can see the difference
in the image when k equal 1 k equal 3
and k equal 5, right? Well the And systems are now able to use
the k-nearest neighbor for visual pattern
recognization to scan and detect hidden packages
in the bottom bin of a shopping cart
at the checkout if an object is detected which matches exactly
to the object listed in the database. Then the price of the spotted
product could even automatically be added
to the customers Bill while this automated
billing practice is not used extensively at this time, but the technology
has been developed and is available for use if you want you can
just use It and yeah, one more thing k-nearest
neighbor is also used in retail to detect patterns in the credit card users many
new transaction scrutinizing software application use Cayenne algorithms to
analyze register data and spot unusual pattern that indicates
suspicious activity. For example,
if register data indicates that a lot
of customers information is being entered manually rather
than through automated scanning and swapping then in that case. This could indicate that the employees
were using the register. In fact stealing customers
personal information or if I register data indicates that a particular good
is being returned or exchanged multiple times. This could indicate that employees are misusing
the return policy or trying to make money from
doing the fake returns, right? So this was about KNN algorithm. So starting with
what is decision tree, but first, let me tell
you why did we choose the Gentry to start with? Well, these decision tree
are really very easy to read and understand it belongs
to one of The few models that interpretable where you can understand exactly
why the classifier has made that particular decision right? Let me tell you a fact
that for a given data set. You cannot say that this algorithm performs
better than that. It's like you cannot say
that decision trees better than a buys or name biases performing better
than decision tree. It depends on the data set, right you have to apply
hit and trial method with all the algorithms one by one and then compare
the result the model which gives the best
result as the Order which you can use
at for better accuracy for your data set. All right, so let's start
with what is decision tree. Well a decision tree is
a graphical representation of all the possible solution to our decision based
on certain conditions. Now, you might be wondering
why this thing is called as decision tree. Well, it is called so because it starts with the root and then branches off
to a number of solution just like a tree right even
the tree starts from a roux and it starts
growing its branches. As once it gets bigger and bigger similarly
in a decision tree. It has a roux which keeps on growing with
increasing number of decision and the conditions now, let me tell you
a real life scenario. I won't say that all of you, but most of you
must have used it. Remember whenever you dial
the toll-free number of your credit card company, it redirects you to his intelligent
computerised assistant where it asks
you questions like, press one for English
or press 2 for Henry, press 3 for this press
4 for that right now once you select one now again, It redirects you
to a certain set of questions like press
1 for this press 1 for that and similarly, right? So this keeps on repeating until you finally get
to the right person, right? You might think that you are caught
in a voicemail hell but what the company
was actually doing it was just using a decision tree
to get you to the right person. I lied. I'd like you to focus
on this particular image for a moment on
this particular slide. You can see I image
where the task is. Should I accept
a new job offer or not? Alright, so you have
to decide that for That what you did you created
a decision tree starting with the base condition
or the root node. Was that the basic salary or the minimum salary
should be $50,000 if it is not $50,000. Then you are not at all
accepting the offer. All right. So if your salary is
greater than $50,000, then you will further check whether the commute is
more than one hour or not. If it is more than one are you
will just decline the offer if it is less than one hour, then you are getting closer
to accepting the job offer then further what you will do. You will check
whether the company is offering. Free coffee or not, right if the company
is not offering the free coffee, then you will just
decline the offer and have fit as offering
the free coffee. And yeah, you will happily
accept the offer right? This is just an example
of a decision tree. Now, let's move ahead
and understand a decision tree. Well, here is a sample data set that I will be using
it to explain you about the decision tree. All right in this data set
each row is an example. And the first two columns
provide features or attributes that describes the data and the last column
gives the label or the class we want to predict and if you like you
can just modify this data by adding additional features and more example and our program will work
in exactly the same way fine. Now this data set
is pretty straightforward except for one thing. I hope you have noticed that
it is not perfectly separable. Let me tell you something
more about that as in the second and fifth examples
they have the same features. But different labels
both have yellow as a Colour and diameter as three, but the labels are mango
and lemon right? Let's move on and see how our decision tree
handles this case. All right, in order to build
a tree will use a decision tree algorithm called card
this card algorithm stands for classification and regression tree
algorithm online. Let's see a preview
of how it works. All right to begin
with We'll add a root node for the tree and all
the nodes receive a list of rows as a input and the route will receive
the entire training data set now each node will ask
true and false question about one other feature. And in response
to that question will split or partition the data set
into two different subsets these subsets then become
input to child node. We are to the tree and the goal of the question
is to finally unmix the labels as we proceed down or in
other words to produce the purest possible distribution
of the labels at each node. For example, the input
of this node contains only. One single type
of label so we could say that it's perfectly unmixed. There is no uncertainty
about the type of label as it consists
of only grapes right on the other hand the labels
in this node are still mixed up. So we would ask another question
to further drill it down, right but before that we need to
understand which question to ask and when and to do that we need to conduct by how much question
helps to unmix the label and we can quantify
the amount of Uncertainty at a single node using
a metric called gini impurity and we can quantify how much a question reduces that uncertainty using a concept
called information game will use these to select the best
question to ask at each point. And then what we'll do
we'll iterate the steps will recursively build the tree on each of the new node
will continue dividing the data until there are
no further question to ask and finally we
reach to our Leaf. Alright, alright,
so this was about decision tree. So in order to create
a diversion First of all what you have to do
you have to identify different set of questions that you can ask to a tree
like is this color green and what will be these question
this question will be decided by your data set like as
this colored green as the diameter greater
than equal to 3 is the color yellow right questions resembles
to your data set remember that? All right. So if my color is green, then what it will do it
will divide into two part first. The Green Mango will be
in the true while on the false. We have lemon
and the map all right. And if the color is green or the diameter is greater
than equal to 3 or the color is yellow. Now let's move on and understand about
decision tree terminologies. Alright, so starting with root node root node
is a base node of a tree the entire tree starts
from a root node. In other words. It is the first node
of a tree it represents the entire population or sample and this entire population
is further segregated or divided into two
or more homogeneous set. Fine. Next is the leaf node. Well, Leaf node is the one when you reach
at the end of the tree, right that is you
cannot further segregated down to any other level. That is the leaf node. Next is splitting splitting
is dividing your root node or node into different sub part
on the basis of some condition. All right, then comes
the branch or the sub tree. Well, this Branch
or subtree gets formed when you split the tree suppose
when you split a root node, it gets divided
into two branches or two subtrees right next. The concept of pruning. Well, you can say
that pruning is just opposite of splitting what we
are doing here. We are just removing
the sub node of a decision tree will see more about pruning
later in this session. All right, let's move on ahead. Next is parent or child node. Well, first of all root node
is always the parent node and all other nodes associated with that
is known as child node. Well, you can understand it
in a way that all the top node belongs to a parent node
and all the bottom node which are derived from
a Top node zhi node the node producing a further note is
a child node and the node which is producing. It is a parent node
simple concept, right? Let's use the cartel Gotham
and design a tree manually. So first of all, what you do you decide
which question to ask and when so
how will you do that? So let's first of all visualize
the decision tree. So there's the decision tree
which will be creating manually or like first of all, let's have a look
at the Data set you have Outlook temperature
humidity and windy as you have different attributes
on the basis of that you have to predict that
whether you can play or not. So which one among them should
you pick first answer determine the best attribute that
classifies the training data? All right. So how will you choose
the best attribute or how does a tree decide where to split or how the tree
will decide its root node? Well before we move on and split a tree there
are some terminologies that you should know. All right first
being the gini index. X so what is this gini Index? This gini index is the measure
of impurity or Purity used in building a decision
Tree in cartel Gotham. All right. Next is Information Gain
this Information Gain is the decrease in entropy after data set is split
on the basis of an attribute constructing a decision tree is
all about finding an attribute that Returns the highest
Information Gain. All right, so you
will be selecting the node that would give you
the highest Information Gain. Alright next is
reduction in variance. Reduction in variance is
an algorithm which is used for continuous Target variable
or regression problems. The split with lower variance
is selected as a criteria to let the population see
in general term. What do you mean by variance? Variance is how much
your data is wearing? Right? So if your data is
less impure or is more pure than in that case
the variation would be less as all the data
almost similar, right? So there's also a way
of setting a tree the split with lower variance is selected as the criteria
to split the population. All right. Next is the chi Square t Square. It is an algorithm which is used to find out
these statistical significance between the differences
between sub nodes and the parent nodes fine. Let's move ahead now
the main question is how will you decide
the best attribute for now just understand that you need to calculate
something known as information game the attribute with the highest Information
Gain is considered the best. Yeah. I know your next question
might be like what? This information, but before we move on and see what exactly Information Gain
Is let me first introduce you to a term called entropy because this term will be used in calculating
the Information Gain. Well entropy is just a metric which measures the impurity
of something or in other words. You can say that as
the first step to do before you solve the problem
of a decision tree as I mentioned is
something about impurity. So let's move on and understand
what is impurity suppose. You are a basket full of apples and another Bowl Which
is full of same label, which says Apple now if you are asked
to pick one item from each basket and ball, then the probability
of getting the apple and it's correct label is 1 so
in this case, you can say that impurities zero. All right. Now what if there are
four different fruits in the basket and four different
labels in the ball, then the probability
of matching the fruit to a label is obviously not one. It's something less than that. Well, it could be possible that I picked banana
from the basket and when I randomly
picked Level from the ball. It says a cherry
any random permutation and combination can be possible. So in this case, I'd say
that impurities is nonzero. I hope the concept
of impurities here. So coming back to entropy as I said entropy is
the measure of impurity from the graph on your left. You can see that as the probability
is zero or one that is either they
are highly impure or they are highly pure
than in that case the value of entropy is zero. And when the probability is
0.5 then the value of entropy. Is maximum. Well, what is impurity
impurities the degree of Randomness how random data is so if the data is completely pure in that case
the randomness equals zero or if the data is completely empty
or even in that case the value of impurity
will be zero question. Like why is it that the value
of entropy is maximum at 0.5 might arise
in a mine, right? So let me discuss about that. Let me derive it mathematically as you can see here on the slide
the mathematical formula of entropy is - of probability of yes, let's move on and see what this graph has to say
mathematically suppose s is our total sample space
and it's divided into two parts. Yes, and no like
in our data set the result for playing was divided
into two parts. Yes or no, which we have to predict
either we have to play or not. Right? So for that particular case, you can Define the formula
of entropy as entropy of total sample
space equals negative of probability of e is multiplied by
log of probability. We of yes, whether base 2 minus probability
of no X log of probability of no with base to where s is
your total sample space and P of v s is
the probability of e s-- and p-- of know is
the probability of no. Well, if the number
of BS equal number of know that is probability
of s equals 0.5 right since you have equal number
of BS and know so in that case the value of entropy will be one just
put the value over there. All right. Let me just move to Next slide
I'll show you this. Alright next is
if it contains all Yes, or all know that is probability
of a sample space is either 1 or 0 then in that case entropy
will be equal to 0 Let's see the
mathematically one by one. So let's start
with the first condition where the probability was 0.5. So this is our formula
for entropy, right? So there's our first case right
which will discuss the art when the probability
of vs equal probability of node that is in our data set we have
Rule number of yes, and no. All right. So probability of yes
equal probability of no and that equals
0.5 or in other words, you can say that yes plus no equal
to Total sample space. All right, since
the probability is 0.5. So when you put the values in the formula you get
something like this and when you calculate it, you will get the entropy of
the total sample space as one. All right. Let's see for the next case. What is the next case
either you have totally us or you have to No, so if you have total, yes, let's see the formula
when we have total. Yes. So you have all yes
and 0 no fine. So probability of e s equal one. And yes as the total
sample space obviously. So in the formula
when you put that thing up here, you get entropy of sample space equal negative X
of 1 multiplied by log of 1 as the value of log 1 equals 0. So the total thing will result
to 0 similarly is the case with no even in that case
you will get the entropy of total sample. Case as 0 so this was
all about entropy. All right. Next is what is
Information Gain? Well Information Gain what it does is it measures
the reduction in entropy. It decides which attribute should be selected
as the decision node. If s is our total collection than Information Gain
equals entropy, which we calculated
just now that - weighted average multiplied
by entropy of each feature. Don't worry. We'll just see how it to calculate
it with an example. All right. So let's manually build
a decision tree for our data set. So there's our data set which consists of
14 different instances out of which we have nine. Yes and five know I like
so we have the formula for entropy just put
over that since 9 years. So total probability
of e s equals 9 by 14 and total probability
of no equals Phi by 14 and when you put up the value and calculate the result
you will get the value. Oh of entropy as 0.94. All right. So this was your first step that is compute the entropy
for the entire data set. All right. Now you have to select that out of Outlook
temperature humidity and windy, which of the node should you
select as the root node big question, right? How will you decide that? This particular node should
be chosen at the base note and on the basis of that only I will be creating
the entire tree. I will select that. Let's see so you have to do it one by one you have
to calculate the entropy and Information Gain for all of the Front note so
starting with Outlook. So Outlook has three different parameters
Sunny overcast and rainy. So first of all select
how many number of years and no are there in the case
of Sunny like when it is sunny how many number of years and how many number
of nodes are there? So in total we have to yes and three Nos and case
of sunny in case of overcast. We have all yes. So if it is overcast then
will surely go to play. It's like that. Alright and next it is rainy
then total number of vs equal. Three and total number
of no equals 2 fine next what we do we
calculate the entropy for each feature for here. We are calculating the entropy
when Outlook equals Sunny. First of all, we are assuming
that Outlook is our root node and for that we are calculating
the information gain for it. Alright. So in order to calculate
the Information Gain remember the formula it was entropy
of the total sample space - weighted average X entropy
of each feature. All right. So what we are doing here, we are calculating
the entropy of out. Look when it was sunny. So total number of yes, when it was sunny was
to and total number of know that was three fine. So let's put up in the formula since the probability
of yes is 2 by 5 and the probability
of no is 3 by 5. So you will get
something like this. Alright, so you are
getting the entropy of sunny as zero point
nine seven one fine. Next we will calculate
the entropy for overcast when it was overcast. Remember it was all yes, right. So the probability of yes is equal 1
and when you put over that you will get the value
of entropy as 0 fine and when it was rainy rainy
has 3s and to nose. So probability of e s
in case of Sonny's 3 by 5 and probability of know
in case of Sonny's 2 by 5. And when you add the value
of probability of vs and probability of no
to the formula, you get the entropy of sunny as
zero point nine seven one point. Now, you have to calculate how much information you
are getting from Outlook that equals weighted average. All right. So what was this? To diverge total number of years
and total number of no fine. So information from Outlook
equals 5 by 14 from where does this 5 came over? We are calculating the total number of sample space
within that particular Outlook when it was sunny, right? So in case of Sunny there
was two years and three NOS. All right. So weighted average for Sonny
would be equal to 5 by 14. All right, since the formula was five
by 14 x entropy of each feature. All right, so as calculated the entropy He
for Sonny is zero point nine. Seven one, right? So what we'll do we'll multiply
5 by 14 with 0.97 one. Right? Well, this was
the calculation for information when Outlook equal sunny, but Outlook even equals overcast
and rainy for in that case. What we'll do again similarly
will calculate for everything for overcast and sunny for overcast weighted averages for by 14 multiplied
by its entropy. That is 0 and for Sonny
it is same Phi by 14. Yes, and to Knows X its entropy that is zero point
nine seven one. And finally we'll take the sum
of all of them which equals to 0.693 right next. We will calculate
the information gained this what we did earlier was
information taken from Outlook. Now, we are calculating. What is the information? We are gaining
from Outlook right. Now this Information Gain that equals to Total entropy
minus the information that is taken from Outlook. All right, so So
total entropy we had 0.94 - information we took
from Outlook as 0.693. So the value of information
gained from Outlook results to zero point two four seven. All right. So next what we have to do. Let's assume that
Wendy is our root node. So Wendy consists of
two parameters false and true. Let's see how many years and how many nodes are there
in case of true and false. So when Wendy has
Falls as its parameter, then in that case it has
six years and to knows. And when it as true
as its parameter, it has 3 S and 3 nodes. All right. So let's move ahead and similarly calculate
the information taken from Wendy and finally calculate the
information gained from Wendy. Alright, so first of all, what we'll do we'll
calculate the entropy of each feature starting
with windy equal true. So in case of true we
had equal number of yes and equal number
of no will remember the graph when we had the probability as 0.5 as total number of years
equal total number of know. For that case
the entropy equals 1 so we can directly
write entropy of room when it's windy is one as we had already proved it when probability equals 0.5
the entropy is the maximum that equals to 1. All right. Next is entropy of false
when it is windy. All right, so similarly just
put the probability of yes and no in the formula
and then calculate the result since you have six years
and two nodes. So in total, you'll get the probability
of e S6 by 8 and probability of know Two by eight. All right, so when you
will calculate it, you will get the entropy of false as zero point
eight one one. Alright, now, let's calculate
the information from windy. So total information
collected from Windy equals information taken when Wendy equal true
plus information taken when when D equals false. So we'll calculate the weighted
average for each one of them and then we'll sum it up to finally get the total
information taken from windy. So in this case, it equals to 8 by 14 multiplied
by 0.8 1 1 + 6 y 14 x 1 what is this? 8 it is total number of yes, and no in case when when D
equals false, right? So when it was false,
so total number of BS that equals to 6 and total more
of know that equal to 2 that some herbs to 8. All right. So that is why the weighted
average results to Aid by 14 similarly information taken when windy equals true equals
to 3 plus 3 that is 3 S and 3 no equal 6 divided by
total number of sample space. That is 14 x That
is entropy of true. All right, so it is a by 14 multiplied by 0.8 1 1
plus 6 by 14 x one which results to 0.89 to this is information taken from Windy. All right. Now how much information
you are gaining from Wendy. So for that what you will do so
total information gained from Windy that equals
to Total entropy - information taken from Windy. All right, that is 0.94 - 0.89 to that equals
to zero point zero four eight. And so 0.048 is the information
gained from Windy. All right. Similarly we calculated
for the rest to all right. So for Outlook
as you can see, the information was 0.693. And it's Information Gain
was zero point two four seven in case of temperature. The information was around zero point nine one one
and the Information Gain that was equal to 0.02
9 in case of humidity. The information gained was 0.15
to and in the case of windy. The information
gained was 0.048. So what we'll do we'll
select the attribute. With a maximum fine. Now, we are selected
Outlook as our root node, and it is further subdivided into three different parts
Sunny overcast and rain, so in case of overcast
we have seen that it consists of all. Yes, so we can consider
it as a leaf node, but in case of sunny and rainy, it's doubtful as it
consists of both. Yes and both know so you need to recalculate
the things right again for this node. You have to
recalculate the things. All right, you have to again
select the attribute. Is having the maximum
Information Gain. All right, so there's how your complete tree
will look like. All right. So, let's see when you can play
so you can play when Outlook is overcast. All right, in that case. You can always play
if the Outlook is sunny. You will further drill down
to check the humidity condition. All right, if the
humidity is normal, then you will play if the humidity is high
then you won't play right when the Outlook predicts that it's rainy then
further you will check whether it's windy or not. If it is a week went then
you will go and offer. Say but if it has strong wind,
then you won't play right? So this is how your entire
decision tree would look like at the end. Now comes the concept
of pruning say is that what should I do to play? Well you have to do
pruning pruning will decide how you will play. What is this pruning? Well, this pruning is nothing
but cutting down the nodes and order to get
the optimal solution. All right. So what pruning does it
reduces the complexity? All right as are you
can see on the screen that it showing only
the result for you. That is it showing all
the result which says that you can play. All right before we drill down
to a practical session a common question
might come in your mind. You might think that our tree base model better
than cleaner model, right? You can think like if I
can use a logistic regression for classification problem and linear regression
for regression problem. Then why there is
a need to use the tree. Well many of us have this In
in their mind and well, there's a valid question too. Well, actually as
I said earlier, you can use any algorithm. It depends on
the type of problem. You're solving let's look
at some key factor, which will help you to decide
which algorithm to use and when so the first point being if the relationship between
dependent and independent variable as well approximated by a linear model then linear regression will outperform
tree base model second case if there is a high
non-linearity and complex relationship between Lent and independent variables
at remodel will outperform a classical regression
model in third case. If you need to build a model which is easy to explain
to people a decision tree model will always do better
than a linear model as the decision tree models are simpler to interpret
then linear regression. All right. Now, let's move on ahead and see how you can write it as
Gentry classifier from scratch and python using
the card algorithm. All right for this. I will be using jupyter notebook
with python 3.0. Oh install on it. Alright, so let's
open the Anaconda and the jupyter notebook. Whereas that so this is a inner Corner Navigator
and I will directly jump over to jupyter notebook and hit
the launch button. I guess everyone
knows that jupyter. Notebook is a web-based
interactive Computing notebook environment where you
can run your python codes. So my jupyter notebook. It opens on my Local
Host double 8 9 1 so I will be using
this jupyter notebook in order to write
my decision tree classifier using python for this
decision tree classifier. I have already written. Set of codes. Let me explain you
just one by one. So we'll start with initializing
our training data set. So there's our sample data set for which each row
is an example. The last column is a label and the first two columns
are the features. If you want you can add some
more features an example for your practice
interesting fact is that this data set
is designed in a way that the second and fifth
example have almost the same features, but they have different labels. All right. So let's move on and see
how the tree handles this case as you can see here both. Both of them the second and the fifth column
have the same features. What did different
is just their label? Right? So let's move ahead. So this is our training data
set next what we are doing we are adding some column labels. So they are used only
to print the trees fine. So what we'll do we'll add
header to the columns like the First Column is
of color second is of diameter and third is a label column. Alright, next Road
will do will Define a function as unique values
in which will pass the rows and the columns. So this function
what it will do. We find the unique values
for a column in the data set. So this is an example for that. So what we are doing here, we are passing
training data Hazard row and column number as 0 so what we are doing we are finding
unique values in terms of color. And in this since the row is training data
and the column is 1 so what you are doing here, so we are finding
the unique values in terms of diameter fine. So this is just an example next what we'll do we'll Define
a function as class count and we'll pass zeros into it. So what it does it counts
the number of each type of Example within data set. So in this function what we are basically doing
we are counting the number of each type for example in the data set or
what we are doing. We are counting the unique
values for the label in the data set as a sample. You can see here. We can pass that entire
training data set to this particular function
as class underscore count what it will do it will find
all the different types of label within the training data set as you can see here the unique
label consists of mango grape and lemon so next what we'll do
we'll Define a function is numeric and we'll pass
a value into it. So what it Do it. We'll just test if the value is numeric
or not and it will return if the value is
an integer or a float. For example, you
can see is numeric. We are passing 7
so it is an integer so it will return in value and if we are passing red it's
not a numeric value, right? So moving on ahead where you define a class
named as question. So what this question does this question is used
to partition the data set. This class voted does it
just records a column number? For example 0 for color a light
and a column value for example, green Next what we are doing
we are defining a match method which is used to compare
the feature value in the example. The feature values
stored in the question. Let's see how first of all
what you are doing. We're defining an init
function and inside that we are passing
the self column and the value as parameter. So next what we do
we Define a function as match what it does is it
compares the feature value in an example to the feature
value in this question when next we'll Define
a function as re PR, which is just a helper method
to print the question in a readable format. Next what we are doing we are
defining a function partition. Well, this function
is used to partition the data set each row
in the data set it checks if it matched
the question or not if it does so it adds it
to the true rose or if not, then it adds to the false Rose. All right, for example, as you can see, it's partition
the training data set based on whether the rows
are ready or not here. We are calling
the function question and we are passing a value
of zero and read to it. So what did we do? It will assign all the red rose
to True underscore Rose. And everything else
will be assigned to false underscore rose fine. Next what we'll do we'll Define
a gini impurity function and inside that will pass
the list of rows. So what it will do it will just
calculate the dream Purity for the list of rows. Next what we are doing
every defining a function as Information Gain. So what this Information Gain
function does it calculates The Information Gain
using the uncertainty of the starting node - the weighted impurity
of the child node. The next function
is find the best plate. Well, this function is used
to find the best question to ask by iterating over
every feature of value and then calculating
the information game. For the detail explanation
on the code. You can find the code
in the description given below. All right next we'll define
a class as leave for classifying the data. It holds a dictionary of glass
like mango for how many times it appears in the row
from the training data that reaches the sleeve. Alright next is
the decision node. So this decision node,
it will ask a question. This holds a reference
to the question and the two child nodes
on the base of that you are deciding which node
to add further to which branch. Alright so next video. We're defining a function
of Beltre and inside that we are passing
our number of rows. So this is the function
that is used to build the tree. So initially what we did we
Define all the various function that we'll be using
in order to build a tree. So let's start by partitioning the data set
for each unique attribute, then we'll calculate
the information gain and then return the question that produces the highest gain and on the basis of that
will split the tree. So what we are doing here, we are partitioning
the data set calculating the Information Gain. And then what this is returning
it is returning the question that is producing
the highest gain. All right. Now if gain equals
0 return Leaf Rose, so what it will do. So if we are getting
no for the gain that is gain equals
0 then in that case since no further question
could be asked so what it will do it
will return a leaf fine now true or underscore Rose or false underscore Rose
equal partition with rose and the question. So if we are reaching
tell this position, then you have already
found a Value which will be used
to partition the data set then what you will do you
will recursively build the true branch and similarly recursively
build the false Branch. So return Division
and Discord node and side that will be passing question
true branch and false Branch. So what it will do it
will return a question node. This question node this
recalls the best feature or the value to ask
at this point fine. Now that we have
Builder tree next what we'll do we'll Define
a print underscore tree function which will be used
to print the tree fine. So finally what we are doing
in this particular function that we are printing our tree
next is the classify function which will use it to decide whether to follow the true
Branch or the false branch and then compared to the feature values stored
in the node to the example. We are considering
and last what we'll do we'll finally print
the production at the leaf. So let's execute
it and see okay, so there's our testing data. Online so we printed
a leaf as well. Now that we have trained
our algorithm is our training data set
now it's time to test it. So there's our testing data set. So let's finally execute
it and see what is the result. So this is the result you
will get so first question, which is asked by the algorithm
is is diameter greater than equal to 3, if it is true, then it will further ask
if the color is yellow again, if it is true, then it will predict mango
as one and lemon with one. And in case it is false, then it will just
predict the mango. Now. This was the true part. Now next coming
to diameter is not greater than or equal to 3 then
in that case it's false. And what did we do? It'll just predict
the grape vine. Okay. So this was all
about the coding part now, let's conclude this session. But before concluding let me
just show you one more thing. Now. There's a scikit-learn
algorithm cheat sheet, which explains you which algorithm you should use
and when all right, let's build in
a decision tree format. At let's see how it is Big. So first condition it will check whether you have
50 samples or not. If your samples
are greater than 50, then we'll move ahead
if it is less than 50, then you need
to collect more data if your sample
is greater than 50, then you have to decide whether you want to predict
a category or not. If you want to
predict a category, then further you will see that whether you
have labeled data or not. If you have label data, then that would be a classification
algorithm problem. If you don't have
the label data, then it would be
a clustering problem. Now if you don't want
to The category then what you want to protect
predict a quantity. Well, if you want
to predict a quantity, then in that case, it would be
a regression problem. If you don't want to predict a quantity and you want
to keep looking further, then in that case, you should go for dimensionality
reduction problems and still if you don't want to look and the predicting structure
is not working. Then you have
tough luck for that. I hope this doesn't recession
clarifies all your doubt over decision tree algorithm. Now, we'll try to find out
the answer to this particular question as to why we
need random Forest fine. So like human beings learn
from the past experiences. So unlike human beings
a computer does not have experiences then how does
machine takes decisions? Where does it learn from? Well a computer system actually learns from the data which
represents some past experiences of an application domain. So now let's see, how random Forest It's
in building up in learning model with a very simple use case
of credit risk detection. Now needless to say that credit card companies have a very nested
interest in identifying Financial transactions that are illegitimate
and criminal in nature. And also I would like
to mention this point that according to
the Federal Reserve payments study Americans used
credit cards to pay for twenty six point
two million purchases in 2012 and The estimated loss
due to unauthorized transactions that here was u.s. 6 point 1 billion dollars now in the banking industry
measuring risk is very critical because the stakes are too high. So the overall goal is
actually to figure out who all can be fraudulent before too much Financial
damage has been done. So for this a credit card
company receives thousands of applications for new cards and each application
contains information. Mission about an
applicant, right? So so here as you can see
that from all those applications what we can actually
figure out is that predictor variables. Like what is the marital
status of the person? What is the gender
of the person? What is the age of the person
and the status which is actually whether it is a default pair
or non-default pair. So default payments are
basically when payments are not made in time and according to the agreement
signed by the cardholder. So now that account is actually
set to be in the default. So you can easily
figure out the history of the particular card holder
from this then we can also look at the time of payment whether he has been
a regular pair or non regular one. What is the source of income
for that particular person and so and so forth. So to minimize loss
the back actually needs certain decision rule to predict whether to approve Particular no one of
that particular person or not. Now here is where the random Forest
actually comes into the picture. All right. Now, let's see how random
Forest can actually help us in this particular scenario. Now, we have taken randomly two parameters out of all
the predictive variables that we saw previously now, we have taken two
predictor variables here. The first one is the income and the second one
is the H right and Hurley parallel it to decision trees
have been implemented upon those predicted variables
and let's first assume the case of the income variable right? So here we have divided
our income into three categories the first one being the person
earning over $35,000 second from 15 to 35 thousand dollars
the third one running in the range of 0 to
15 thousand dollars. Now if a person
is earning over $35,000, which is a pretty Good
income pretty decent. So now we'll check out
for the credit history. And here the probability is that if a person is earning
a good amount then there is very low risk that he won't be able to pay
back already earning good. So the probability is that his application
of loan will get approved. Right? So there is actually low risk
or moderate risk, but there's no real issue
of higher risk as such. We can approve
the applicants request here. Now, let's move on and watch out
for the second category where the person
is actually earning from 15 to 35 thousand dollars
right now here the person may or may not pay back. So in such scenarios will look
for the credit history as to what has been
his previous history. Now if his previous
history has been bad like he has been a default ER
in the previous transactions will definitely not Consider
approving his request and he will be at the high risk in which
is not good for the bank. If the previous history of that particular
applicant is really good. Then we will just to clarify a doubt will consider
another parameter as well that will be on depth. I have his already
in really high dip then the risks again increases
and there are chances that he might not pay
repay in the future. So here Will. Not accept the request
of the person having high dipped if the person is
in the low depth and he has been a good pair
in his past history. Then there are chances that he might be back
and we can consider approving the request
of this particular applicant. Alex look at the third category, which is a person earning
from 0 to 15 thousand dollars. Now, this is something
which actually raises I broke and this person
will actually lie in the category of high risk. All right. So the probability is that his application of loan
would probably get rejected now, we'll get one final outcome from
this income parameter, right? Now let us look
at our second variable that is H which will lead
into the second decision tree. Now. Let us say
if the person is Young, right? So now we will look forward to
if it is a student now if it is a student then
the chances are high that he won't be
able to repay back because he has
no earning Source, right? So here the risks are too high
and probability is that his application
of loan will get rejected fine. Now if the person is Young and his Not the student
then we'll probably go on and look for another variable. That is pan balance. Now. Let's look if the bank balance
is less than 5 lakhs. So again the risk arises
and the probabilities that his application
of loan will get rejected. Now if the person
is Young is not a student and his bank balance so of greater than 5 lakhs
is got a pretty good and stable and balanced
then the probability is that he is sort of application will get approved of Now
let us take another scenario if he's a senior, right? So if he is a senior
will probably go and check out for this credit history. How well has he been
in his previous transactions? What kind of a person he is like whether he's a defaulter
or is Ananda falter. Now if he is a very
fair kind of person in his previous transactions
then again the risk arises and the probability
of his application getting rejected actually
increases right now if he has An excellent person as per his transactions
in the previous history. So now again here
there is least risk and the probabilities that his application
of loan will get approved. So now here these two variables
income and age have led to two different decision trees. Right and these two different
decision trees actually led to two different results. Now what random forest does is
it will actually compile these two different results
from these two different. Gentry's and then finally, it will lead
to a final outcome. That is how random
Forest actually works. Right? So that is actually the motive
of the random Forest. Now let us move forward and see
what is random Forest right? You can get an idea of the mechanism from the name
itself random forests. So a collection
of trees is a fortress that's why I called
for is probably and here also the trees are actually
because being trained on subsets which are being
selected at random. And therefore they are called
random forests So Random forests is a collection or an insane. Humble of decision trees right
here decision trees actually built using the whole data
set considering all features, but actually in random Forest
only a fraction of the number of rows is selected and that too at random and a particular
number of features, which are actually selected
at random are trained upon and that is how the decision trees
are built upon. Right? So similarly number
of decision trees will be grown and each decision tree will Salt
into a certain final outcome and random Forest
will do nothing but actually just
compiled the results of all those decision trees
to bring up the final result. As you can see
in this particular figure that a particular instance
actually has resulted into three different
decision trees, right? So not tree one results into
a final outcome called Class A and tree to results into class B. Similarly tree
three results into class P So Random Forest will compile the results
of all these Decision trees and it will go by the call
of the majority voting now since head to decision trees
have actually voted into the favor of the Class B
that is decision tree 2 and 3. Therefore the final outcome will
be in the favor of the Class B. And that is how random
Forest actually works upon. Now one really beautiful thing about
this particular algorithm is that it is one
of the versatile algorithms which is capable of Performing
both regression as well as Now, let's try to understand
random Forest further with a very beautiful example
or this is my favorite one. So let's say you want to decide if you want to watch edge
of tomorrow or not, right? So in this particular scenario, you will have two different
actions to work Bond either. You can just straight away go to your best friend
asked him about. All right, whether should I go for Edge
of Tomorrow not will I like this movie or you
can ask Your friends and take their opinion
consideration and then based on the final results who can go out and watch Edge
of Tomorrow, right? So now let's just take
the first scenario. So where you go
to your best friend asked about whether you should go
out to watch edge of tomorrow or not. So your friend will probably
ask you certain questions like the first one being
here Jonah So so let's say your friend asks you if you really like
The Adventurous kind of movies or not. So you say yes, definitely I would love to watch
it Venture kind of movie. So the probabilities that you will like edge
of tomorrow as well. Since Age of Tomorrow is
also a movie of Adventure and sci-fi kind
of Journal right? So let's say you do not like
the adventure John a movie. So then again
the probability reduces that you might really
not like edge of Morrow right. So from here you can come
to a certain conclusion right? Let's say your best friend puts
you into another situation where he'll ask you or a do you like Emily Blunt
and you see definitely I like Emily Blunt and then he
puts another question to you. Do you like Emily Blunt
to be in the main lead and you say yes, then again, the probability arises that you will definitely
like edge of tomorrow as well because Edge of Tomorrow
is Has the Emily plant in the main lead cast so and if you say oh I do not like
Emily Blunt then again, the probability reduces that you would like Edge
of Tomorrow to write. So this is one way where you have one decision tree
and your final outcome. Your final decision will be
based on your one decision tree, or you can see your final
outcome will be based on just one friend. No, definitely not
really convinced. You want to consider the options
of your other friends also so that you can make
very precise and crisp decision right you go out and you approach some other
bunch of friends of yours. So now let's say you go
to three of your friends and you ask them
the same question whether I would like to watch
it off tomorrow or not. So you go out and approach three or four friends friend
one friend twin friend three. Now, you will consider
each of their Sport and then you will your decision
now will be dependent on the compiled results of all
of your three friends, right? Now here, let's say you go
to your first friend and you ask him whether you would like
to watch it just tomorrow not and your first friend
puts you to one question. Did you like Top Gun? And you say yes, definitely I did like the movie
Top Gun then the probabilities that you would like
edge of tomorrow as well because topgun is actually
a military action drama, which is also Tom Cruise. So now again the probability
Rises that yes, you will like edge
of tomorrow as well and If you say no I didn't like
Top Gun then again. The chances are that you wouldn't like Edge
of Tomorrow, right? And then another question
that he puts you across is that do you really like
to watch action movies? And you say yes, I would love to watch
them that again. The chances are that you would like
to watch Edge of Tomorrow. So from your friend when you can come
to one conclusion now here since the ratio of liking the movie
to don't like is actually 2 is to 1 so the final
result is Actually, you would like Edge of Tomorrow. Now you go to your second friend
and you ask the same question. So now you are second friend
asks you did you like far and away when we went
out and did the last time when we washed it and you say no I really
didn't like far and away then you would say then
you are definitely going to like Edge of Tomorrow. Why does so because far
and away is actually since most of whom
might not be knowing it so far in a ways Johner of romance and it revolves around a girl and a guy By falling in love
with each other and so on. So the probability is that you wouldn't like
edge of tomorrow. So he ask you another question. Did you like Bolivian and to really like
to watch Tom Cruise? And you say Yes, again. The probability is that you would like
to watch Edge of Tomorrow. Why because Oblivion
again is a science fiction casting Tom Cruise full
of strange experiences. And where Tom Cruise is
the savior of the masses. Kind well, that is the same kind of plot
in edge of tomorrow as well. So here it is pure yes that you would like
to watch edge of tomorrow. So you get another second decision
from your second friend. Now you go to your third
friend and ask him so probably our third friend is
not really interesting in having any sort
of conversation with you say, it just simply asks you did you
like Godzilla and you said no I didn't like Godzilla's we said definitely
you wouldn't like it's of tomorrow why so because Godzilla is also
actually sign Fiction movie from the adventure Jonah. So now you have got
three results from three different decision trees
from three different friends. Now you compile the results
of all those friends and then you make
a final call that yes, would you like to watch edge
of tomorrow or not? So this is some very real time
and very interesting example where you can actually
Implement random Forest into ground reality right
any questions so far. So far, no, that's good, and then
we can move forward. Now let us look
at various domains where random Forest
is actually used. So because of its diversity
random Forest is actually used in various diverse to means like so beat banking beat medicine beat land use
beat marketing name it and random Forest is there so
in banking particularly random Forest is being
actually used to make it out whether the applicant
will be a default a pair or it Will be non default of 1 so that it can accordingly approve or reject
the applications of loan, right? So that is how random Forest
is being used in banking talking about medicine. Random. Forest is widely used in medicine field
to predict beforehand. What is the probability if a person will actually have
a particular disease or not? Right? So it's actually used to look
at the various disease Trends. Let's say you want to figure
out what is the probability that a person
will have diabetes? Not and so what would you do? It'd probably look
at the medical history of the patient and then
you will see or read. This has been
the glucose concentration. What was the BMI? What was the insulin levels in the patient in the past
previous three months. What is the age
of this particular person and will make a different
decision trees based on each one of these predictor variables and then you'll finally
compiled the results of all those variables
and then you'll make a fine. Final decision as to whether the person
will have diabetes in the near future or not. That is how random
Forest will be used in medicine sector now move. Random Forest is also actually
used to find out the land use. For example, I want to set
up a particular industry in certain area. So what would I probably
look for a look for? What is the
vegetation over there? What is the Urban
population over there? Right and how much is the Is
from the nearest modes of Transport like
from the bus station or the railway station
and accordingly. I will split my parameters and I will make decision
on each one of these parameters and finally I'll compile
my decision of all these parameters in that
will be my final outcome. So that is how I
am finally going to predict whether I should put my industry at this particular
location or not. Right? So these three examples
have actually been of majorly around classification problem because we are
trying to classify whether or not we're actually
trying to answer this question whether or not right now, let's move forward and look how marketing is revolving
around random Forest. So particularly in marketing we try to identify
the customer churn. So this is particularly
the regression kind of problem right now how let's see so customer churn is nothing but actually
the number of people which are actually
The number of customers who are losing out. So we're going
out of your market. Now you want to identify what will be your customer churn
in near future. So you'll most of them eCommerce Industries are
actually using this like Amazon Flipkart Etc. So they particularly look
at your each Behavior as to what has been your past history. What has been
your purchasing history. What do you like
based on your activity around certain things around
certain ads around certain? Discounts or around certain kind
of materials right? If you like a particular top
your activity will be more around that particular top. So that is how they track each
and every particular move of yours and then
they try to predict whether you will be
moving out or not. So that is how they identify
the customer churn. So these all are various domains where random Forest
is used and this is not the only list so there
are numerous other examples which are Chile are using random forests that makes
it so special actually. Now, let's move
forward and see how random Forest actually works. Right. So let us start with the random
Forest algorithm first. Let's just see it step by step as to how random
Forest algorithm works. So the first step is
to actually select certain M features from T. Where m is less than T. So here T is the total number
of the predictor variables that you have in your data set and out of
those total predictor variables. You will select some randomly
some Features out of those now why we are actually selecting
a few features only. The reason is that if you will select all
the predictive variables or the total predictor variables
then each of your decision tree will be same. So the model is not actually
learning something new. It is learning
the same previous thing because all those decision trees
will be similar, right if you actually split
your predicted variables and you select randomly
a few predicted variables only. Let's say there are 14 total
number of variables and out of those you randomly
pick just three right? So every time you will get
a new decision tree, so there will be variety. Right? So the classification model
will be actually much more intelligent
than the previous one. Now. It has got
barrier to experiences. So definitely it will make
different decisions each time. And then when you will compile
all those different decisions, it will be a new more accurate. An efficient result right? So the first important step
is to select certain number of features out of all
the features now, let's move on to
the second step. Let's say for any node D. Now. The first step is to calculate
the best plate at that point. So, you know that decision tree how decision trees
actually implemented so you pick up a the most
significant variable right? And then you will split
that particular node into Other child nodes that is how the split
takes place, right? So you will do it
for M number of variables that you have selected. Let's say you
have selected three so you will implement
the split at all. Those three nodes
in one particular decision tree, right the third step
is split up the node into two daughter nodes. So now you can split
your root note into as many notes as you want to put hair
will split our node into 2.2 notes as to this or that so it will be an answer
in terms of You saw that right? Our fourth step will be
to repeat all these 3 steps that we've done previously and we'll repeat
all this splitting until we have reached all
the N number of nodes. Right? So we need to repeat until we have reached
till the leaf nodes of a decision tree. That is how we will do it right
now after these four steps. We will have
our one decision tree. But random Forest is
actually about multiple. Asian trees. So here our fifth step
will come into the picture which will actually repeat
all these previous steps for D number of times now
hit these the D number of decision trees. Let's say I want to implement
five decision trees. So my first step will be to implement all
the previous steps 5 times. So the head the eye tration is
4/5 number of times right now. Once I have created these five decision trees still
my task is not complete yet. On my final task will be
to compile the results of all these five
different decision trees and I will make a call in the majority
voting right here. As you can see in this picture. I had in different instances. Then I created
n different decision trees. And finally I will compile
the result of all these n different decision trees and I will take my call
on the majority voting right. So whatever my
majority vote says that will be My final result. So this is basically an overview
of the random Forest algorithm how it actually works. Let's just have a look
at this example to get much better understanding
of what we have learnt. So let's say I have
this data set which consists of four
different instances, right? So basically it consists
of the weather information of previous 14 days right
from D1 tildy 14, and this basically
Outlook humidity and wind is Click gives me
the better condition of those 14 days. And finally I have play which is my target variable
weather match did take place on that particular day
or not right. Now. My main goal is to find out whether the match
will actually take place if I have following
these weather conditions with me on any particular day. Let's say the Outlook
is rainy that day and humidity is high
and the wind is very weak. So now I need to predict whether I will be able
to play in the match. That they are not. All right. So this is
a problem statement fine. Now, let's see how random Forest
is used in this to sort it out. Now here the first step
is to actually split my entire data set
into subsets here. I have split my entire
14 variables into further smaller subsets right
now these subsets may or may not overlap like there is certain
overlapping between d 1 till D3 and D3 till D6 fine. Is an overlapping of D3 so it might happen
that there might be overlapping so you need not really worry
about the overlapping but you have to make sure that all those subsets are
actually different right? So here I have taken
three different subsets my first subset consists of D1 till D3 Mexican subset
consists of D3 till D6 and methods subset
consists of D7 tildy. Now now I will first be focusing
on my first upset now here, let's say that particular day the Outlook was
Overcast fine if yes, it was overcast
then the probabilities that the match will take place. So overcast is basically
when your weather is too cloudy. So if that is the condition
then definitely the match will take place and let's say
it wasn't overcast. Then you will consider these
second most probable option that will be the wind and you will make
a decision based on this now whether wind was weak or strong
if wind was weak, then you will definitely
go out and play them. Judge as you would not so
now the final outcome out of this decision
tree will be Play Because here the ratio
between the play and no play is to is to 1 so we get to a certain decision
from a first decision tree. Now, let us look
at the second subset now since second subset has
different number of variables. So that is why this decision
trees absolutely different from what we saw in our four subsets. So let's say if it was overcast
then you will play the match if It isn't the overcast
in you would go and look out for humidity. Now further. It will get split into two
whether it was high or normal. Now, we'll take the first case if the humidity was high
and when it was week, then you will play
the match else if humidity was high
but wind was too strong, then you would not go out
and play the match right now. Let us look at the second dot
to node of humidity if the humidity was normal. The wind was weak. Then you will definitely go out
and play the match as you want go out
and play the match. So here if you look
at the final result, then the ratio of placed no play
is 3 is to 2 then again. The final outcome
is actually play, right? So from second subset, we get the final
decision of play now, let us look at our third subset which consists of D7
till D9 here if again the overcast is yes,
then you will play a match. Each else you will go
and check out for humidity. And if the humidity is
really high then you won't play the match else. You will play the match
again the probability of playing the matches. Yes, because the ratio
of no play is Twist one, right? So three different subsets
three different decision trees three different outcomes and one final outcome
after compiling all the results from these three different
decision trees are so I This gives a better perspective
better understanding of random Forest like
how it really works. All right. So now let's just have a look at various features
of random Forest Ray. So the first
and the foremost feature is that it is one of the most accurate
learning algorithms, right? So why it is so because single decision trees
are actually prone to having high variance or Hive bias and on
the contrary actually. M4s, it averages
the entire variance across the decision trees. So let's say if the variances say
X4 decision tree, but for random Forest, let's say we have
implemented n number of decision trees parallely. So my entire variance
gets averaged to upon and my final variance
actually becomes X upon n so that is how the entire variance
actually goes down as compared to other algorithms. Now second most
important feature is that it works well
for both classification and regression problems and by far I have come
across this is one and the only algorithm which works equally
well for both of them these classification kind
of problem or a regression kind of problem, right? Then it's really runs efficient
on large databases. So basically it's
really scalable. Even if you work for
the lesser amount of database or if you work for a really
huge volume of data, right? So that's a very
good part about it. Then the fourth most
important point is that it requires almost
no input preparation. Now, why am I saying this is because it has got
certain implicit methods, which actually take care
and All the outliers and all the missing data and you really don't have to
take care about all that thing while you are in the stages
of input preparations. So Random Forest is
all here to take care of everything else and next. Is it performs implicit
feature selection, right? So while we are implementing
multiple decision trees, so it has got implicit method which will automatically pick
up some random features out. Of all your parameters
and then it will go on and implementing
different decision trees. So for example, if you just give
one simple command that all right, I want to implement
500 decision trees no matter how so Random Forest
will automatically take care and it will Implement all
those 500 decision trees and those all 500 decision trees
will be different from each other and this is because it has
got implicit methods which will automatically
collect different parameters. Out of all the variables
that you have right? Then it can be easily grown
in parallel why it is so because we are actually implementing multiple
decision trees and all those decision trees are running or all those decisions
trees are actually getting implemented parallely. So if you say I want thousand
trees to be implemented. So all those thousand trees are
getting implemented parallely. So that is how the computation
time reduces down. Right, and the last point is that it has got methods
for balancing error in unbalanced it as it's now what exactly
unbalanced data sets are let me just give
you an example of that. So let's say you're working
on a data set fine and you create a random
forest model and get 90% accuracy immediately. Fantastic you think right. So now you start diving
deep you go a little deeper. And you discovered that 90% of that data actually
belongs to just one class damn your entire data set. Your entire decision
is actually biased to just one particular class. So Random Forest actually
takes care of this thing and it is really not biased towards any particular decision
tree or any particular variable or any class. So it has got methods
which looks after it and they does is all the balance
of errors in your data sets. So that's pretty much about the features
of random forests. What is KNN algorithm will K. Nearest neighbor
is a simple algorithm that stores all
the available cases and classify the new data or case based
on a similarity measure. It suggests that if you are similar
to your neighbors, then you are one of them, right? For example, if apple looks more similar
to banana orange or Melon. Rather than a monkey rat or a cat then most likely Apple
belong to the group of fruits. All right. Well in general Cayenne is used
in Search application where you are looking
for similar items that is when your task is
some form of fine items similar to this one. Then you call this search
as a Cayenne search. But what is this KN KN? Well this K denotes the number
of nearest neighbor which are voting class
of the new data or the testing data. For example, if k equal 1 then the testing
data are given the same label as a close this Ample
in the training set similarly if k equal to 3 the labels of the three closes classes
are checked and the most common label is assigned
to then testing data. So this is what a KN KN algorithm means
so moving on ahead. Let's see some
of the example of scenarios where KN is used
in the industry. So, let's see
the industrial application of KNN algorithm starting
with recommender system. Well the biggest use case of cayenne and search
is a recommender system. This recommended system is
like an automated form of a shop counter guy when you
asked him for a product. Not only shows you the product but also suggest you or displays
your relevant set of products, which are related to the item. You're already interested
in buying this KNN algorithm applies to recommending
products like an Amazon or for recommending media, like in case of Netflix or even
for recommending advertisement to display to a user if I'm not wrong almost all
of you must have used Amazon for shopping, right? So just to tell you more
than 35% of amazon.com revenue is generated by
its recommendation engine. So what's their
strategy Amazon uses? Recommendation as
a targeted marketing tool in both the email campaigns around most of its website Pages Amazon will
recommend many products from different categories based
on what you have browser and it will pull those products
in front of you which you are likely to buy like the frequently
bought together option that comes at the bottom
of the product page to tempt you into buying the combo. Well, this recommendation
has just one main goal that is increase average
order value or to upsell and cross-sell customers
by providing product suggestion based on items
in the shopping cart, or On the product they are
currently looking at on site. So next industrial
application of KNN algorithm is concept search or searching semantically
similar documents and classifying documents
containing similar topics. So as you know, the data on the Internet
is increasing exponentially every single second. There are billions and billions
of documents on the internet each document on the internet
contains multiple Concepts, that could be
a potential concept. Now, this is a situation where the main problem
is to extract concept from a set of documents as each page could have
thousands of combination that could be potential Concepts
an average document could have millions of concept combined that the vast amount
of data on the web. Well, we are talking
about an enormous amount of data set and Sample. So what we need is we need
to find the concept from the enormous amount
of data set and samples, right? So for this purpose, we'll be using KNN
algorithm more advanced example could include handwriting
detection like an OCR or image recognization
or even video recognization. All right. So now that you know
various use cases of KNN algorithm, let's proceed and see
how does it work. So how does
a KNN algorithm work? Let's start by plotting
these blue and orange point on our graph. So these Blue Points
the belong to class A and the orange ones
they belong to class B. Now you get a star as a new pony
and your task is to predict whether this new point
it belongs to class A or it belongs to the class B. So to start the production,
the very first thing that you have to do is
select the value of K, just as I told you KN KN
algorithm refers to the number of nearest neighbors that you
want to select for example, in this case k equal to 3. So what does it mean it means that I am selecting three points which are the least distance
to the new point or you can say I am selecting
three different points which are closest to the star. Well at this point
of time you can ask how will you calculate
the least distance? So once you
calculate the distance, you will get one blue
and two orange points which are closest to this star
now since in this case as we have a majority
of Inch point so you can see that for k equal 3D star
belongs to the class B, or you can say that the star is more similar
to the orange points moving on ahead. Well, what if k equal
to 6 well for this case, you have to look
for six different points which are closest to this star. So in this case
after calculating the distance, we find that we have
four blue points and two Orange Point which are closest
to the star now, as you can see that the blue points are
in majority so you can say that for k equals
6 this star belongs. These two class A or the star
is more similar to Blue Points. So by now, I guess you know
how a KNN algorithm work. And what is the significance
of gain KNN algorithm. So how will you
choose the value of K? So keeping in mind this case
the most important parameter in KNN algorithm. So, let's see when you build
a k nearest neighbor classifier. How will you choose
a value of K? Well, you might have
a specific value of K in mind or you could divide up
your data and use something like cross-validation technique
to test several values of K in order to determine which works best for your data. Example if n equal
2,000 cases then in that case the optimal value
of K lies somewhere in between 1 to 19. But yes, unless you try it
you cannot be sure of it. So, you know how the algorithm
is working on a higher level. Let's move on and see how things are predicted
using KNN algorithm. Remember I told you the KNN algorithm uses
the least distance measure in order to find
its nearest neighbors. So let's see
how these distances calculated. Well, there are
several distance measure which can be used. So to start with Will mainly
focus on euclidean distance in Manhattan distance
in this session. So what is
this euclidean distance? Well, this euclidean distance
is defined as the square root of the sum of difference
between a new point x and an existing Point why so for example here we
have Point P1 and P2 Point P. 1 is 1 1 and point B 2 is 5 for so what is the euclidean
distance between both of them? So you can say
that euclidean distance is a direct distance
between two points. So what is the distance
between the point P1 and P2? So we Calculate it as
5 minus 1 whole square plus 4 minus 1 whole square and we can route it
over which results to 5. So next is
the Manhattan distance. Well, this Manhattan distance is
used to calculate the distance between real Vector using the sum of their absolute
difference in this case. The Manhattan distance
between the point P1 and P2 is mod of 5 minus 1
plus mod value of 4 minus 1, which results to 3 plus 4. That is 7. So this slide shows
the difference between euclidean and Manhattan distance
from point A to point B. So euclidean distance is
nothing but the direct or the least possible distance
between A and B. Whereas the Manhattan distance
is a distance between A and B measured along the axis
at right angle. Let's take an example and see how things are predicted
using KNN algorithm or how the cannon
algorithm is working suppose. We have data set
which consists of height weight and T-shirt size
of some customers. Now when a new customer
come we only have is height. And wait as the information
now our task is to predict. What is the T-shirt size
of that particular customer? So for this will be using
the KNN algorithm. So the very first thing
what we need to do, we need to calculate
the euclidean distance. So now that you have a new data
of height 160 one centimeter and weight as 61 kg. So the very first thing that we'll do is we'll calculate
the euclidean distance, which is nothing
but the square root of 160 1 minus 158 whole square plus 61 minus 58 whole square
and square root of that is 4.24. Let's drag and drop it. So these are the various
euclidean distance of other points. Now, let's suppose k equal
to 5 then the algorithm what it does is it searches
for the five customer closest to the new customer that is most similar
to the new data in terms of its attribute for k equal 5. Let's find the top five
minimum euclidian distance. So these are the distance which we are going
to use one two, three, four and five. So let's rank them
in the order first. This is second. This is third then this one
is Forward and again, this one is five. So there's our order. So for k equal 5 we
have for t-shirts which come under size
M and one t-shirt which comes under size l so obviously best guess
for the best prediction for the T-shirt size of white
161 centimeters and wait 60 1 kg is M. Or you can say
that a new customer fit into size M. Well this was all
about the theoretical session. But before we drill down
to the coding part, let me just tell you why people
call KN as a lazy learner. Well KN for classification. Ocean is a very
simple algorithm. But that's not why they are
called lazy KN is a lazy learner because it doesn't have
a discriminative function from the training data. But what it does it
memorizes the training data, there is no learning phase
of the model and all of the work happens at the time. Your prediction is requested. So as such there's the reason
why KN is often referred to us lazy learning algorithm. So this was all about
the theoretical session now, let's move on
to the coding part. So for the Practical implementation of
the Hands-On part, I'll be using
the artists data set so This data set consists
of 150 observation. We have four features and one class label
the four features include the sepal length sepal width
petal length and the petrol head whereas the class label
decides which flower belongs to which category. So this was the description
of the data set, which we are using now, let's move on and see
what are the step by step solution
to perform a KNN algorithm. So first, we'll start
by handling the data what we have to do we
have to open the data set from the CSV format and split the data set
into train and test part next. We'll take the Clarity where we
have to calculate the distance between two data instances. Once we calculate the distance
next we'll look for the neighbor and select K Neighbors which are having the least
distance from a new point. Now once we get our neighbor, then we'll generate a response
from a set of data instances. So this will decide whether the new Point belongs
to class A or Class B. Finally will create
the accuracy function and in the end. We'll tie it all together
in the main function. So let's start with our code for implementing KNN
algorithm using python. I'll be using Java. Old book by Don
3.0 installed on it. Now. Let's move on and see how can an algorithm
can be implemented using python. So there's my jupyter notebook, which is a web-based interactive
Computing notebook environment with python 3.0 installed on it. So the launch its launching so
there's our jupyter notebook and we'll be riding
our python codes on it. So the first thing that we need to do is
load our file our data is in CSV format
without a header line or any code we can open
the file the open function and read the data line
using the reader function. In the CSV module. So let's write a code
to load our data file. Let's execute the Run button. So once you execute
the Run button, you can see the entire training
data set as the output next. We need to split the data
into a training data set that KN can use to make
prediction and a test data set that we can use to evaluate
the accuracy of the model. So we first need to convert
the flower measure that will load it as
string into numbers that we can work next. We need to split the data set
randomly to train and test. Ratio 67's 233 for test is
to train as a standard ratio, which is used for this purpose. So let's define a function as load data set that loads a CSV
with the provided file named and split it
randomly into training and test data set using
the provided split ratio. So this is our function load
data set which is using filename split ratio training data set and testing data
set as its input. All right. So let's execute the Run button
and check for any errors. So it's executed
with zero errors. Let's test this function. So there's our training
set testing set load data set. So this is our function
load data set on inside that we are passing. Our file is data
with a split ratio of 0.66 and training data set
and test data set. Let's see what our training data
set and test data set. It's dividing into so
it's giving a count of training data set
and testing data set. The total number
of training data set as split into is
97 and total number of test data set we have is 53. So total number of training data
set we have here is 97 and total number of test data
set we have here is 53. All right. Okay, so Function load
data set is performing. Well, so let's move
on to step two which is similarity. So in order to make prediction, we need to calculate
the similarity between any two given data instances. This is needed so that we can locate the kamo
similar data instances in the training data set are
in turn make a prediction given that all for flower measurement
are numeric and have same unit. We can directly use
the euclidean distance measure. This is nothing
but the square root of the sum of squared differences
between two areas of the number given that all the for flower
Are numeric and have same unit we can directly use
the euclidean distance measure which is nothing
but the square root of the sum of squared difference
between two areas or the number additionally
we want to control which field to include
in the distance calculation. So specifically we only want
to include first for attribute. So our approach will be
to limit the euclidean distance to a fixed length. All right. So let's define
our euclidean function. So this are euclidean
distance function which takes instance
one instance to and length as parameters instance 1 and ends. These two are the two points of which you want to calculate
the euclidean distance, whereas this length and denote that how many attributes
you want to include? Okay. So there's our
euclidean function. Let's execute it. It's executing fine
without any errors. Let's test the function
suppose the data one or the first instance consists
of the data point has two to two and it belongs to class A and data to consist
of four for four and it belongs to class P. So when we calculate
the euclidean distance of data one to data to and what we have to do we
have to consider only first three features of them. All right. So let's print the distance
as you can see here. The distance comes
out to be three point four six four now like
so this is nothing but the square root
of 4 minus 2 whole Square. So this distance is nothing
but the euclidean distance and it is calculated as square
root of 4 minus 2 whole square plus 4 minus 2 whole square that is nothing but 3
times of 4 minus 2 whole square that is 12 + square root of 12 is nothing
but 3.46 for all right. So now that we have calculated
the distance now we need to look for K nearest. Neighbors now that we
have a similarity measure we can use it to collect
the kamo similar instances for a given unseen instance. Well, this is
a straightforward process of calculating the distance
for all the instances and selecting a subset with
the smallest distance value. And now what we have
to do we have to select the smallest distance values. So for that will be
defining a function as get neighbors. So for that what we will be doing
will be defining a function as get neighbors what it will do it will return
the K most similar Neighbors From the training set
for a given test instance. All right, so this is
how our get neighbors In look like it takes training data set and test instance
and K as its input here. The K is nothing but the number of nearest neighbor
you want to check for. All right. So basically what
you'll be getting from this get Mabel's
function is K different points having least euclidean distance
from the test instance. All right, let's execute it. So the function executed
without any errors. So let's test our function. So suppose the training data set
includes the data like to to to and it belongs to class A and other data includes
four four four and it belongs to class P and at testing
and Census 555 or now, we have to predict whether this test instance
belongs to class A or it belongs to class be. All right for k equal 1
we have to predict its nearest neighbor and predict whether this test instance
it will belong to class A or will it belong to class be? Alright. So let's execute the Run button. All right. So an executing
the Run button you can see that we have output
as for for for and be a new instance 5 5 5 is closes 2.44
for which belongs to class be. All right. Now once you have located
the most similar neighbor for a test instance next task
is to predict a response based on those neighbors. So how we can do that. Well, we can do this by allowing each neighbor
to vote for the class attribute and take the majority vote
as a prediction. Let's see how we can do that. So we are function as getresponse with takes
neighbors as the input. Well, this neighbor was nothing
but the output of this get me / function the output of get me were function
will be fed to get response. All right. Let's execute the Run button. It's executed. Let's move ahead and test
our function get response. So we have a But as bun bun
bun it belongs to class A 2 2 2 it belongs to class a33. It belongs to class B. So this response,
that's what it will do. It will store the value
of get response by passing this neighbor value. I like so what we want
to check is we want to predict whether that test instance
final outcome will belongs to class A or Class B. When the neighbors are
1 1 1 a 2 2 A + 3 3 B. So, let's check our response. Now that we have created
all the different function which are required
for a KNN algorithm. So important main concern is how do you evaluate
the accuracy of the prediction and easy way to evaluate the accuracy of the model
is to calculate a ratio of the total correct prediction
to all the protection made. So for this I will
be defining function as get accuracy and inside that I'll be passing
my test data set and the predictions get
accuracy function check get executed without any error. Let's check it
for a sample data set. So we have our test data set as
1 1 1 It belongs to class A 2/2 which again belongs to class
3 3 3 which belongs to class B and my predictions is
for first test data. It predicted latter belongs
to class A which is true for next it predicted
that belongs to class C, which is again to and for
the next again and predictive that it belongs to class A
which is false in this case cause the test data
belongs to class be. All right. So in total we have to correct
prediction out of three. All right, so the ratio
will be 2 by 3, which is nothing but 66.6. So our accuracy rate is 66.6. It's so now that you
have created all the function that are required
for KNN algorithm. Let's compile them
into one single main function. Alright, so this is
our main function and we are using Iris data set with a split of 0.67 and
the value of K is 3 Let's see. What is the accuracy score
of this check how accurate are modulus so
in training data set, we have a hundred
and thirteen values and then the test data set. We have 37 values. These are the predicted and the actual values
of the output. Okay. So in total we got
an accuracy of 90s. In point two nine percent,
which is really very good. Alright, so I hope the concept
of this KNN algorithm is here device in a world
full of machine learning and artificial intelligence
surrounding almost everything around us classification and prediction is one of the most important aspects
of machine learning. So before moving forward, let's have a quick look
at the agenda. I'll start off this video
by explaining you guys what exactly is Nave biased then we'll and what
is Bayes theorem which serves as a logic behind the name pass
algorithm going forward. I'll explain the steps involved in the neighbors
algorithm one by one and finally add finish
of this video with a demo on the Nave bass using
the SQL own package noun a bass is a simple but
surprisingly powerful algorithm from penetrative analysis. It is a classification technique
based on base theorem with an assumption of
Independence among predictors. It comprises of two parts,
which is name. And bias in simple terms
neighbors classifier assumes that the presence
of a particular feature in a class is unrelated
to the presence of any other feature, even if this features
depend on each other or upon the existence
of the other features, all of these properties
independently contribute to the probability whether a fruit is an apple
or an orange or a banana. So that is why it is known as naive now naive
based model is easy to build and particularly useful
for very large data sets. In probability Theory
and statistics based theorem, which is already
known as the base law or the base rule describes
the probability of an event based on prior knowledge
of the conditions that might be related
to the event now paste theorem is a way to figure
out conditional probability. The conditional probability
is the probability of an event happening given that it has some relationship
to one or more other events. For example, your probability
of getting a parking space is connected to the time
of the day you pass. Where you park and what conventions are you
going on at that time based Serum is slightly
more nuanced in a nutshell. It gives you an actual
probability of an event given information about the tests. Now, if you look
at the definition of Bayes theorem, we can see
that given a hypothesis H and the evidence
e-base term states that the relationship between the
probability of the hypothesis before getting the evidence which is the P of H
and the probability of the hypothesis
after getting the evidence that P of H given e
is defined as probability of e given H into probability of H divided by probability of e
it's rather confusing, right? So let's take an example
to understand this theorem. So suppose I have
a deck of cards and if a single card is drawn
from the deck of playing cards, the probability that the card
is a king is for by 52 since there are four Kings
in a standard deck of 52 cards. Now if King is an event,
this card is a king. The probability of King
is given as 4 by 52 that is equal to 1 by 13. Now if the evidence is provided
for instance someone looks as the That the single card
is a face card the probability of King given that it's a face
can be calculated using the base theorem
by this formula. The since every King
is also a face card the probability of face given that it's a king is equal to 1 and since there are
three face cards in each suit. That is the chat king and queen. The probability of the face card
is equal to 12 by 52. That is 3 by 30. Now using Bayes theorem we
can find out the probability of King given that it's a face so our final answer
comes to 1 by 3, which is also true. So if you have a deck of cards which has having only faces now
there are three types of phases which are the chat king
and queen so the probability that it's the king is 1 by 3. Now. This is the simple example
of how based on works now if we look at the proof as in
how this Bayes theorem Evolved. So here we have
probability of a given p and probability of B
given a now for a joint probability distribution
over the sets A and B, the probability of
a intersection B, the conditional probability
of a given B is defined as the probability of a intersection B divided
by probability of B, and similarly probability of B, given a is defined as
probability of B intersection a divided by probability
of a now we can Equate probability of
a intersection p and probability of B intersection a as
both are the same thing now from this method as you can see, we get our final
base theorem proof, which is the probability of a
given b equals probability of B, given a into probability of P divided by
the probability of a now while this is the equation that applies to
any probability distribution over the events A and B. It has a particular nice
interpretation in case where a is represented
as the hypothesis h and B is represented as some observed evidence e
in that case the formula is p of H given e is equal to P of e given H into probability of H divided
by probability of e now this relates the probability of hypotheses before
getting the evidence, which is p of H
to the probability of the hypothesis
after getting the evidence which is p of H given e for this reason P of H is known
as the prior probability while P of Each given e is known
as the posterior probability and the factor that relates the two is known as
the likelihood ratio Now using this term space theorem
can be rephrased as the posterior
probability equals. The prior probability
times the likelihood ratio. So now that we know the maths which is involved
behind the baster. Mm. Let's see how we can implement
this in real life scenario. So suppose we have a data set. In which we have
the Outlook the humidity and we need to find out whether we should play
or not on that day. So the Outlook can be
sunny overcast rain and the humidity high normal and the wind are categorized
into two phases which are the weak
and the strong winds. The first of all will create
a frequency table using each attribute of the data set. So the frequency table
for the Outlook looks like this we have Sunny overcast
and rainy the frequency table of humidity looks like this and Frequency table of when
looks like this we have strong and weak for wind and high
and normal ranges for humidity. So for each frequency table, we will generate
a likelihood table now now the likelihood table
contains the probability of a particular day
suppose we take the sunny and we take the play as yes and no so the probability
of Sunny given that we play yes is 3 by 10, which is 0.3 the
probability of X, which is the
probability of Sunny Is equal to 5 by 14 now, these are all the terms which are just generated
from the data which we have a and finally the probability
of yes is 10 out of 14. So if we have a look
at the likelihood of yes given that it's a sunny we
can see using Bayes theorem. It's the probability
of Sunny given yes into probability of s divided
by the probability of Sunny. So we have all
the values here calculated. So if you put
that in our base serum equation, we get the likelihood of yes. A 0.59 similarly the likelihood of no can also be calculated
here is 0.40 now similarly. We are going to create
the likelihood table for both the humidity and the win there's a for humidity the likelihood
for yes given the humidity is high is equal to 0.4
to and the probability of playing know
given the vent is high is 0.58. The similarly for table wind
the probability of he has given that the wind is week is 0.75
and the probability of no given that the win is week is 0.25
now suppose we have of day which has high rain which has high humidity
and the wind is weak. So should we play or not? That's our for that? We use the base theorem
here again the likelihood of yes on that day is equal to the probability
of Outlook rain given that it's a yes into probability
of Magic given that say yes, and the probability of
when that is we given that it's we are playing yes
into the probability of yes, which equals to zero
point zero one nine and similarly the likelihood
of know on that day is equal to zero point zero one six. Now if we look at the probability
of yes for that day of playing we just
need to divide it with the likelihood
some of both the yes and no so the probability
of playing tomorrow, which is yes is 5 whereas the probability
of not playing is equal to 0.45. Now. This is based upon the data
which we already have with us. So now that you have an idea
of what exactly is named bias how it works and we have seen how it can be implemented
on a particular data set. Let's see where it
is used in the industry. The started with our first
industrial use case, which is news categorization
or we can use the term text classification
to broaden the spectrum of this algorithm news in the web are rapidly growing
in the era of Information Age where each new site has
its own different layout and categorization
for grouping news. Now these heterogeneity of layout and categorization
cannot always satisfy individual users need
to remove these heterogeneity and classifying
the news articles. Owing to the user preference
is a formidable task companies use web crawler
to extract useful text from HTML Pages
the news articles and each of these news articles is then tokenized now
these tokens are nothing but the categories
of the news now in order to achieve
better classification result. We remove the less
significant Words, which are the stop was
from the documents or the Articles and then we apply
the Nave base classifier for classifying the news
contents based on the news. Now this is by far one of the best examples
of Neighbors classifier, which is Spam filtering. Now. It's the Nave
Bayes classifier are a popular statistical technique
for email filtering. They typically use bag
of words features to identify at the spam email and approach commonly used
in text classification as well. Now it works by correlating
the use of tokens, but the spam and non-spam emails
and then the Bayes theorem, which I explained earlier is used to
calculate the probability that an email is or not a Spam so named
by a Spam filtering is a baseline technique
for dealing with Spam that container itself to the emails need
of an individual user and give low false positive
spam detection rates that are generally
acceptable to users. It is one of the oldest ways
of doing spam filtering with its roots in the 1990s particular words
have particular probabilities of occurring in spam. And and legitimate email
as well for instance. Most emails users will frequently encounter
the world lottery or the lucky draw a spam email, but we'll sell them
see it in other emails. The filter doesn't know
these probabilities in advance and must be friends. So it can build them
up to train the filter. The user must manually indicate whether a new email is Spam
or not for all the words in each straining email. The filter will
adjust the probability that each word will appear
in a Spam or legitimate. Owl in the database now after training the word
probabilities also known as the likelihood functions are
used to compute the probability that an email with a particular
set of words as in in belongs to either category each word in the email contributes
the email spam probability. This contribution is called
the posterior probability and is computed again
using the base 0 then the email spam probability is computed over all
the verse in the email and if the total exceeds
a certain threshold say Or 95% the filter will Mark
the email as spam. Now object detection is
the process of finding instances of real-world objects
such as faces bicycles and buildings in images or video now object detection algorithm typically
use extracted features and learning algorithm to recognize instance of
an object category here again, a bass plays an important
role of categorization and classification of object
now medical area. This is increasingly voluminous
amount of electronic data, which are becoming more
and more complicated. The produced medical data
has certain characteristics that make the analysis
very challenging and attractive as well among all
the different approaches. The knave bias is used. It is the most effective
and efficient classification algorithm and has
been successfully applied to many medical problems
empirical comparison of knave bias versus
five popular classifiers on Medical data sets shows that may bias is well suited
for medical application and has high performance in most
of the examine medical problems. Now in the past various
testicle methods have been used for modeling in the area
of disease diagnosis. These methods require
prior assumptions and are less capable of dealing with massive and complicated
nonlinear and dependent data one of the main advantages
of neighbor as approach which is appealing
to Physicians is that all the available
information is used? To explain the decision
this explanation seems to be natural for medical
diagnosis and prognosis. That is it is very
close to the way how physician diagnosed patients
now weather is one of the most influential factor
in our daily life to an extent that it may affect
the economy of a country that depends on occupation
like agriculture. Therefore as a countermeasure
to reduce the damage caused by uncertainty
in whether Behavior, there should be an efficient way
to print the weather now whether projecting
has Challenging problem in the meteorological department since ears even
after the technology skill and scientific
advancement the accuracy and protection of weather
has never been sufficient even in current day this domain
remains as a research topic in which scientists and mathematicians are working
to produce a model or an algorithm that will accurately
predict the weather now a bias in approach
based model is created by where posterior probabilities
are used to calculate the likelihood of
each class label for input. Data instance and the one
with the maximum likelihood is considered as the resulting
output now earlier. We saw a small implementation
of this algorithm as well where we predicted whether we should play
or not based on the data, which we have collected earlier. Now, this is a python Library which is known as scikit-learn
it helps to build in a bias and model in Python. Now, there are three types
of named by ass model under scikit-learn Library. The first one is the caution. It is used in classification
and it Assumes that the feature follow
a normal distribution. The next we have is multinomial. It is used for discrete counts. For example, let's say we have
a text classification problem and here we
consider bernouli trials, which is one step further and instead of word
occurring in the document. We have count how often word occurs in the document you
can think of it as a number of times
outcomes number is observed in the given number of Trials. And finally we have
the bernouli type. Of neighbors. The binomial model is useful if your feature vectors are
binary bag of words model where the once and the zeros are words occur
in the document and the verse which do not occur in the document respectively
based on their data set. You can choose any of
the given discussed model here, which is the gaussian
the multinomial or the bernouli. So let's understand
how this algorithm works. And what are
the different steps? One can take to create
a bison model and use knave bias to predict the output so
here to understand better. We are going to predict
the onset of diabetes Now this problem comprises of 768 observations
of medical details for Pima Indian patients. The record describes
instantaneous measurement taken from the patient such as
the age the number of times pregnant and the blood work crew now all
the patients are women aged 21 and Older and all
the attributes are numeric and the unit's vary
from attribute to attribute. Each record has
a class value that indicate whether the patient suffered
on onset of diabetes within five years
are the measurements. Now. These are classified as 0 now. I've broken the whole process
down into the following steps. The first step
is handling the data in which we load the data
from the CSV file and split it into training and test it as it's the second step
is summarizing the data. In which we summarize the properties in the training
data sets so that we can calculate the probabilities
and make predictions. Now the third step comes is
making a particular prediction. We use the summaries of the data set to generate
a single prediction. And after that we generate
predictions given a test data set and a summarized
training data sets. And finally we evaluate the accuracy of the predictions
made for a test data set as the percentage correct
out of all the predictions made and finally We tied
together and form. Our own model
of nape is classifier. Now. The first thing we need to do
is load our data the data is in the CSV format
without a header line or any codes. We can open the file
with the open function and read the data lines
using the read functions in the CSV module. Now, we also need
to convert the attributes that were loaded as
strings into numbers so that we can work with them. So let me show you how this can be implemented now
for that you need to Tall python on a system and use
the jupyter notebook or the python shell. Hey, I'm using
the Anaconda Navigator which has all the things required to do
the programming in Python. We have the Jupiter lab. We have the notebook. We have the QT console. Even we have a studio as well. So what you need to do is just
install the Anaconda Navigator it comes with the pre
installed python also, so the moment you click launch
on The jupyter Notebook. It will take you
to the Jupiter homepage in a local system and here you
can do programming in Python. So let me just rename it as
by my India diabetes. So first, we need
to load the data set. So I'm creating here a function
load CSV now before that. We need to import
certain CSV the math and the random method. So as you can see, I've created a load CSV function which will take the pie
my Indian diabetes data dot CSV file using
the CSV dot read a method and then we are converting
every element of that data set into float originally all
the ants are in string, but we need to convert
them into floor for all calculation purposes. The next we need to split
the data into training data sets that nay bias can use
to make the prediction and this data set that we can use to evaluate
the accuracy of the model. We need to split the data
set randomly into training and testing data set
in the ratio of usually which is 7230. But for this example, I'm going to use 67 and 33 now 70 and 30 is a Ratio
for testing algorithms so you can play around
with this number. So this is our split
data set function. Now the Navy base
model is comprised of summary of the data
in the training data set. Now this summary is then used
while making predictions. Now the summary
of the training data collected involves the mean
the standard deviation of each attribute
by class value now, for example, if there are two class values
and seven numerical attributes, then we need a mean and the standard deviation for
each of these seven attributes and the class value which makes The 14
attributes summaries so we can break the preparation of this summary down
into the following sub tasks which are the separating data
by class calculating mean calculating standard deviation
summarizing the data sets and summarizing
attributes by class. So the first task is to separate the training data set
instances by class value so that we can calculate
statistics for each class. We can do that by creating a map of each class value
to a list of instances that belong to the class. Class and sort the entire
dataset of instances into the appropriate list. Now the separate
by class function just the same. So as you can see
the function assumes that the last attribute
is the class value the function returns a map
of class value to the list of data instances next. We need to calculate
the mean of each attribute for a class value. Now, the mean is the central middle or
the central tendency of the data and we use it as a middle
of our gaussian distribution when Calculating
the probabilities. So this is our function
for mean now. We also need to calculate
the standard deviation of each attribute
for a class value. The standard deviation
is calculated as a square root of the variance and the variance
is calculated as the average of the squared differences for each attribute value from the mean now
one thing to note that here is that we are using
n minus one method which subtracts one from the number
of attributes values when calculating the variance. Now that we have the tools
to summarize the data for a given list of instances. We can calculate the mean
and standard deviation for each attribute. Now that's if function groups
the values for each attribute across our data instances
into their own lists so that we can compute the mean
and standard deviation values for each attribute. Now next comes the summarizing
attributes by class. We can pull it all together
by first separating. Our training data sets
into instances groped by class then calculating the summaries
for each a Should be now. We are ready to make predictions
using the summaries prepared from our training data making patients involved
calculating the probability that a given data instance
belong to each class then selecting the class with the largest probability
as a prediction. Now we can divide this whole
method into four tasks which are the calculating
gaussian probability density function calculating class
probability making a prediction and then estimating the accuracy now to calculate the gaussian
probability density function. We use the gaussian function
to estimate the probability of a given attribute value
given the node mean and the standard deviation
of the attribute estimated from the training data. As you can see
the parameters RX mean and the standard deviation now in the calculate
probability function, we calculate the exponent first
then calculate the main division this lets us fit the equation
nicely into two lines. Now, the next task is calculating the
class properties now that we had can calculate
the probability of an attribute belonging to a class. We can combine the probabilities
of all the attributes values for a data instance
and come up with a probability of the entire. Our data instance
belonging to the class. So now that we have calculated
the class properties. It's time to finally make
our first prediction now, we can calculate the probability
of the data instance belong to each class value and we can look
for the largest probability and return the associated class and for that we are going
to use this function predict which uses the summaries and the input Vector which is
basically all the probabilities which are being input
for a particular label now finally we can
An estimate the accuracy of the model
by making predictions for each data instances
in our test data for that. We use the get
predictions method. Now this method is used to calculate the predictions
based upon the test data sets and the summary
of the training data set. Now, the predictions
can be compared to the class values
in our test data set and classification accuracy
can be calculated as an accuracy ratio
between the zeros and the hundred percent. Now the get accuracy method will
calculate this accuracy ratio. Now finally to sum it all up. We Define our main function
we call all these methods which we have defined
earlier one by one to get the Courtesy of the model
which we have created. So as you can see, this is our main function
in which we have the file name. We have defined the split ratio. We have the data set. We have the training
and test data set. We are using the split
data set method next. We are using the summarized
by class function using the get protection and
the get accuracy method as well. So guys as you can see
the output of this one gives us that we are splitting
the 768 Rose into 514 which is the training and 254 which is the test data set rows
and the accuracy of this model is 68% Now we can play
with the amount of training and test data sets
which are to be used so we can change
the split ratio to seventies. 238 is 220 to get
different sort of accuracy. So suppose I change
the split ratio from 0.67 20.8. So as you can see, we get the accuracy
of 62 percent. So splitting it into 0.67
gave us a better result which was 68 percent. So this is how you can Implement
Navy bias caution classifier. These are the step
by step methods which you need to do in case of
using the Nave Bayes classifier, but don't worry. We do not need to write
all this many lines of code to make a model
this with the second. And I really comes into picture
the scikit-learn library has a predefined method or as say a predefined
function of nape bias, which converts all
of these lines, of course into merely just
two or three lines of codes. So, let me just open
another jupyter notebook. So let me name it
as sklearn a pass. Now here we are going to use
the most famous data set which is the iris De Casa. Now, the iris flower data
set is a multivariate data set introduced by
the British statistician and biologists Roland Fisher and based on this fish is linear
discriminant model this data set became a typical test case for many statistical
classification techniques in machine learning. So here we are going to use
the caution NB model, which is already available
in the sklearn. As I mentioned earlier, there were three
types of Neighbors which are the question
multinomial and the bernouli. So here we are going to use
the caution and be model which is already present
in the SK loan Library, which is the cycle in library. So first of all, what we need to do is
import the sklearn data sets and the metrics and we also need
to import the caution NB Now once all these libraries are lowered we need
to load the data set which is the iris dataset. The next what we need
to do is fit a Nave by a smaller to this data set. So as you can see we have so
easily defined the model which is the gaussian
NB which contains all the programming which I just showed you
earlier all the methods which are taking the input
calculating the mean the standard deviation
separating it bike last and finally making predictions. Calculating the
prediction accuracy. All of this comes
under the caution and be method which is inside already present
in the sklearn library. We just need to fit it
according to the data set which we have so next if we print the model we see
which is the gaussian NB model. The next what we need to do
is make the predictions. So the expected output
is data set dot Target and the projected
is using the pretend model and the model we are using
is the cause in N be here. Now to summarize the model which created we calculate
the confusion Matrix and the classification report. So guys, as you can see
the classification to provide we have the Precision
of Point Ninety Six, we have the recall of 0.96. We have the F1 score and the support and finally if
we print our confusion Matrix, as you can see it gives
us this output. So as you can see
using the gaussian and we method just
putting it in the model and using any of the data. fitting the model which you created
into a particular data set and getting the desired
output is so easy with the scikit-learn library as we Mo support
Vector machine is one of the most effective
machine learning classifier and it has been used
in various Fields such as face recognition
cancer classification and so on today's session is dedicated to how svm works
the various features of svm and how it Is used
in the real world. All right. Okay. Now let's move on and see
what svm algorithm is all about. So guys s VM or support Vector machine is
a supervised learning algorithm, which is mainly used to classify
data into different classes now unlike most algorithms svm
makes use of a hyperplane which acts like
a decision boundary between the various classes in general svm can
be used to generate multiple separating hyperplanes so that the data
is Divided into segments. Okay, and each of these segments will contain
only one kind of data. It's mainly used for classification purpose
wearing you want to classify or data into two different
segments depending on the features of the data. Now before moving any further, let's discuss a few
features of svm. Like I mentioned earlier svm is
a supervised learning algorithm. This means that svm trains on a set of labeled data svm
studies the label training data and then classifies
any new input Data, depending on what it learned in the training phase
a main advantage of support Vector machine is that it can be used
for both classification and regression problems. All right. Now even though svm is mainly
known for classification the svr which is the support
Vector regressor is used for regression problems. All right, so svm can be used
both for classification. And for regression. Now, this is one of the reasons
why a lot of people prefer svm because it's a very
good classifier and along That it is also
used for regression. Okay. Another feature is the svm
kernel functions svm can be used for classifying nonlinear data by using the kernel trick
the kernel trick basically means to transform your data
into another dimension so that you can easily
draw a hyperplane between the different
classes of the data. Alright, nonlinear data
is basically data which cannot be separated
with a straight line. Alright, so svm can even be used
on nonlinear data sets. You just have to use
a A kernel functions to do this. All right. So guys, I hope
you all are clear with the basic concepts of svm. Now, let's move on
and look at how svm works so there's an order
to understand how svm Works let's consider a small scenario
now for a second pretend that you own a firm. Okay, and let's say
that you have a problem and you want to set up a fence
to protect your rabbits from the pack of wolves. Okay, but where do you build your films
one way to get around? The problem is to build
a classifier based. On the position of the rabbits
and words in your pasture. So what I'm telling you is
you can classify the group of rabbits as one group and draw a decision
boundary between the rabbits and the world correct. So if I do that and if I try
to draw a decision boundary between the rabbits
and the Wolves, it looks something like this. Okay. Now you can clearly build
a fence along this line in simple terms. This is exactly how SPM work it draws
a decision boundary, which is a hyperplane between any New classes
in order to separate them or classify them now. I know you're thinking
how do you know where to draw a hyperplane the basic principle behind
svm is to draw a hyperplane that best separates
the two classes in our case the two glasses
of the rabbits and the Wolves. So you start off by drawing
a random hyperplane and then you check the distance
between the hyperplane and the closest data points from each Club these closes
on your is data points to the hyperplane are known
as support vectors. And that's where the name comes
from support Vector machine. So basically the
hyperplane is drawn based on these support vectors. So guys an optimal
hyperplane will have a maximum distance from each
of these support vectors. All right. So basically the hyperplane
which has the maximum distance from the support vectors is
the most optimal hyperplane and this distance
between the hyperplane and the support vectors
is known as the margin. All right, so to sum it up svm
is used to classify data. By using a hyper plane such
that the distance between the hyperplane and
the support vectors is maximum. So basically your margin
has to be maximum. All right, that way, you know that you're actually
separating your classes or add because the distance between
the two classes is maximum. Okay. Now, let's try
to solve a problem. Okay. So let's say that I input
a new data point. Okay. This is a new data point and now I want to draw
a hyper plane such that it best separates
the two classes. Okay, so I start off
by drawing a hyperplane. Like this and then
I check the distance between the hyperplane
and the support vectors. Okay, so I'm trying to check if the margin is maximum
for this hyper plane, but what if I draw a hyperplane
which is like this? All right. Now I'm going to check
the support vectors over here. Then I'm going
to check the distance from the support vectors and for
this hyperplane, it's clear that the margin is more red. When you compare the margin
of the previous one to this hyperplane. It is more. So the reason why I'm choosing
this hyperplane is because the Distance
between the support vectors and the hyperplane is maximum
in this scenario. Okay. So guys, this is
how you choose a hyperplane. You basically have to make sure that the hyper plane
has a maximum. Margin. All right, it has to best
separate the two classes. All right. Okay so far it was quite easy. Our data was linearly separable which means that you
could draw a straight line to separate the two classes. All right, but what will you do? If the data set is like this you possibly can't draw
a hyperplane like Is on it, it doesn't separate
the two classes at all. So what do you do in such situations now earlier
in the session I mentioned how a kernel can be used
to transform data into another dimension that has a clear dividing margin
between the classes of data. Alright, so kernel functions
offer the user this option of transforming nonlinear spaces
into linear ones. Nonlinear data set is the one that you can't separate
using a straight line. All right. In order to deal
with such data sets, you're going to transform them
into linear data sets and then use svm on them. Okay. So simple trick would be
to transform the two variables X and Y into a new
feature space involving a new variable called Z. All right, so guys so far
we were plotting our data on two dimensional space. Correct? We will only using the X and the y axis so we had only
those two variables X and Y now in order to deal with this kind
of data a simple trick. Be to transform
the two variables X and Y into a new feature space
involving a new variable called Z. Okay, so we're basically
visualizing the data on a three-dimensional space. Now when you transform
the 2D space into a 3D space you can clearly see
a dividing margin between the two classes
of data right now. You can go ahead
and separate the two classes by drawing the best
hyperplane between them. Okay, that's exactly what we discussed
in the previous slides. So guys, why don't you
try this yourself dried. Drawing a hyperplane, which is the most Optimum
for these two classes. All right, so guys, I hope you have
a good understanding about nonlinear svm's now. Let's look at a real
world use case if support Vector machines. So guys s VM as a classifier has been used
in cancer classification since the early 2000s. So there was an experiment held
by a group of professionals who applied svm in a colon
cancer tissue classification. So the data set
consisted of about Transmembrane protein samples and only about 50 to 200
genes samples were input Into the svm classifier Now this sample which was input into the svm classifier had
both colon cancer tissue samples and normal colon tissue
samples right now. The main objective of this study
was to classify Gene samples based on whether they
are cancerous or not. Okay, so svm was trained
using the 50 to 200 samples in order to discriminate
between non-tumor from A tumor specimens. So the performance of the svm classifier
was very accurate for even a small data set. All right, we had only
50 to 200 samples and even for the small data set
svm was pretty accurate with this results. Not only that its
performance was compared to other classification
algorithms like naive Bayes and in each case svm
outperform naive Bayes. So after this experiment
it was clear that svm classified the data
more effectively and it worked exceptionally good. Small data sets. Let's go ahead and understand what exactly
is unsupervised learning. So sometimes the given data
is unstructured and unlabeled so it becomes difficult
to classify the data into different categories. So unsupervised learning
helps to solve this problem. This learning is used
to Cluster the input data and classes on the basis
of their statistical properties. So example, we can cluster Different Bikes based
upon the speed limit there. Acceleration or the average that they are giving so and suppose learning is a type
of machine learning algorithm used to draw inferences from beta sets consisting
of input data without labeled responses. So if you have a look
at the workflow or the process flow
of unsupervised learning, so the training data is
collection of information without any label. We have the machine
learning algorithm and then we have
the clustering malls. So what it does is that distributes the data
into a different class. And again, if you provide
any unreliable new data, it will make a prediction and find out to which cluster
that particular data or the data set belongs to or the particular data point
belongs to so one of the most important algorithms in unsupervised
learning is clustering. So let's understand exactly
what is clustering. So a clustering basically is the process
of dividing the data sets into groups consisting
of similar data points. It means grouping
of objects based on the information found in
the data describing the object. Objects or their relationships so clustering malls focus on and defying groups
of similar records and labeling records according to the group to which
they belong now this is done without the benefit
of prior knowledge about the groups
and their characteristics. So and in fact, we may not even know exactly
how many groups are there to look for. Now. These models are often
referred to as unsupervised learning models, since there's no external
standard by which to judge. One is
classification performance. There are no right or wrong
answers to these model. And if we talk about why
clustering is used so the goal of clustering
is to determine the intrinsic group in a set
of unlabeled data sometime. The partitioning is the goal or the purpose of clustering
algorithm is to make sense of and exact value from the last set of structured
and unstructured data. So that is why clustering
is used in the industry and if you have a look at the video, These use cases of clustering
in the industry. So first of all,
it's being used in marketing. So discovering distinct groups in customer databases
such as customers who make a lot
of long-distance calls customers who use internet more than cause they're also
using insurance companies. So like I need to find groups
of Corporation insurance policy holders with high average
claim rate Farmers crash cops, which is profitable. They are using C Smith studies
and defined problem areas of Oil or gas exploration
Based on seesmic data, and they're also used
in the recommendation of movies. If you would say they
are also used in Flickr photos. They also used by Amazon for recommending the product
which category it lies in. So basically if we talk about clustering there are
three types of clustering. So first of all, we have the exclusive clustering which is the hard clustering
so here and item belongs exclusively to one cluster
not several clusters and the data point. Along exclusively
to one cluster. So an example of this is
the k-means clustering so k-means clustering
does this exclusive kind of clustering so secondly, we have overlapping clustering so it is also known as
soft clusters in this and item can belong to multiple clusters as
its degree of association with each cluster
is shown and for example, we have fuzzy
or the c means clustering which is being used
for overlapping clustering and finally we have
The hierarchical clustering so when two clusters have
a parent-child relationship or a tree-like structure, then it is known
as hierarchical cluster. So as you can see here
from the example, we have a parent-child kind of relationship in
the cluster given here. So let's understand what exactly is
K means clustering. So k-means clustering is
an algorithm whose main goal is to group similar elements
of data points into a cluster and it is the process by which objects are classified
into a predefined number. Of groups so that they
are as much dissimilar as possible from one group
to another group but as much as similar or
possible within each group now if you have a look at the
algorithm working here, right? So first of all, it starts with and defying
the number of clusters, which is k then I can we find the centroid we find
the distance objects to the distance object to the centroid distance
of object to the centroid then we find the Dropping based on the minimum distance has
the centroid Converse if true then we make
a cluster false. We then I can't find
the centroid repeat all of the steps
again and again, so let me show you how exactly clustering was
with an example here. So first we need
to decide the number of clusters to be made now
another important task here is how to decide the important
number of clusters or how to decide the number
of clusters really get into that later. So first, let's assume that the number Number
of clusters we have decided is 3 so after that then
we provide the centroids for all the Clusters which is guessing and the algorithm calculates
the euclidean distance of the point from each centroid and assigns the data point to the closest cluster
now euclidean distance. All of you know
is the square root of the distance the square root
of the square of the distance. So next when the center
is a calculated again, we have our new clusters
for each data point. And again the distance
from the points to the new clusters
are calculated and then again, the points are assigned
to the closest cluster. And then again, we have the new centroid scattered and now
these steps are repeated until we have
a repetition the centroids or the new center eyes are very
close to the very previous ones. So antenna and less
output gets repeated or the outputs are
very very close enough. We do not stop this process. We keep on calculating
the euclidean distance. It's of all the points
to the centroids. Then we calculate
the new centroids and that is how clay means
clustering Works basically, so an important part
here is to understand how to decide the value of K
or the number of clusters because it does
not make any sense. If you do not know how many classes
are you going to make? So to decide
the number of clusters, we have the elbow method. So let's assume first of all compute
the sum squared error, which is the sse4 some value. A for example, let's take two four six
and eight now the SS e which is the sum squared
is defined as a sum of the squared distance
between each number member of the cluster and its centroid
mathematically and if you mathematically it
is given by the equation which is provided here. And if you brought
the key against the SSE, you will see
that the error decreases as K gets large now this is because the number
of cluster increases they should be smaller. So does this torsion is
also smaller know the idea of the elbow method is
to choose the K at which the SSC decreases abruptly. So for example here if we have a look
at the figure given here. We see that the best number
of cluster is at the elbow as you can see here the graph
here changes abruptly after number four. So for this particular example, we're going to use
for as a number of cluster. So first of all while working with k-means clustering
there are two key points, As to know first of all
be careful about various start. So choosing the first center at random choosing
the second center that is far away from the first
center similarly choosing the NIH Center as far away
as possible from the closest of the all the other centers and the second idea
is to do as many runs of k-means each with different
random starting points so that you get an idea
of where exactly and how many clusters
you need to make and where exactly the centroid lies. And how the data
is getting confused now k-means is not exactly
a very good method. So let's understand the pros and
cons of clay means clusterings. We know that k-means is simple
and understandable. Everyone loves you that the first go
the items automatically assigned to the Clusters. Now if we have
a look at the cons, so first of all one needs to
define the number of clusters, there's a very
heavy task asks us if we have 3/4 or if we have 10 categories
and if we do not know what the number
of clusters are going to be. It's Difficult for anyone
to you know to guess the number of clusters not all items
are forced into clusters whether they are actually belong
to any other cluster or any other category, they are forced to to lie in that other category
in which they are closest to this against happens
because of the number of clusters with not defining
the correct number of clusters or not being able to guess
the correct number of clusters. So and most of all it's unable to handle the noisy data and
the outliners because anyway, As machine learning engineers and data scientists
have to clean the data. But then again it comes down to the analysis watch they
are doing and the method that they are using so typically
people do not clean the data for k-means clustering even if the clean there's
sometimes a now see noisy and outliners data
which affect the whole model so that was all
for k-means clustering. So what we're going to do
is now use k-means clustering for the We data set so we have to find out
the number of clusters and divide it accordingly. So the use case is
that first of all, we have a data set
of five thousand movies. And what you want
to do is grip them if the movies into clusters
based on the Facebook likes, so guys, let's have a look
at the demo here. So first of all, what we're going to do is
import deep copy numpy pandas Seaborn the various libraries, which we're going to use now
and from map popular videos. In the use ply plot, and we're going to use
this ggplot and next what we're going to do
is import the data set and look at the shape
of the data is it so if we have a look at the
shape of the data set we can see that it has 5043 rows
with Twenty Eight columns. And if you have a look at the head of the data set we
can see it has 5043 data points, so What we're going to do
is place the data points in the plot me take
the director Facebook likes and we have a look
at the data columns face number and post cars total Facebook likes director
Facebook likes. So what we have done here now is taking the director
Facebook likes and the actor three Facebook likes, right. So we have five thousand
forty three rows and two columns Now using
the k-means from sklearn what we're going
to do is import it. First we're going
to import k-means from sklearn dot cluster. Remember guys Escalon is
a very important library in Python for machine learning. So and the number of cluster what we're going to do is
provide as five now this again, the number of cluster
depends upon the SSE, which is the sum
of squared errors or the we're going
to use the elbow method. So I'm not going to go
into the details of that again. So we're going to fit the data
into the k-means to fit and if you find the cluster, Us then for the
k-means and printed. So what we find is is
an array of five clusters and Fa print the label
of the Caymans cluster. Now next what we're going
to do is plot the data which we have with the Clusters
with the new data clusters, which we have found
and for this we're going to use the si bon and
as you can see here, we have plotted that car. We have plotted
the data into the grid and You can see here. We have five clusters. So probably what I would say is that the cluster
3 and the cluster zero are very very close. So it might depend
see that's exactly what I was going to say. Is that initially
the main Challenge and k-means clustering is
to define the number of centers which are the K. So as you can see here that the third Center and the zeroth cluster
the third cluster and the zeroth cluster up
very very close to each other so It probably could have been
in one another cluster and the another disadvantage was that we do not exactly know how the points are
to be arranged. So it's very difficult to force
the data into any other cluster which makes our analysis
a little different works fine. But sometimes it
might be difficult to code in the k-means clustering now, let's understand what exactly
is seems clustering. So the fuzzy c means is an extension of the k-means
clustering the popular simple. Clustering technique so
fuzzy clustering also referred as soft clustering is a form of clustering in which
each data point can belong to more than one cluster. So k-means tries to find
the heart clusters where each point belongs
to one cluster. Whereas the fuzzy c means
discovers the soft clusters in a soft cluster
any point can belong to more than one cluster at a time with
a certain Affinity value towards each 4zc means assigns
the degree of membership, which Just from 0 to 1
to an object to a given cluster. So there is a stipulation
that the sum of the membership of an object to all the cluster. It belongs to must be equal
to 1 so the degree of membership of this particular point to pull
of these clusters as 0.6 0.4. And if you add up we get 1 so that is one of the logic
behind the fuzzy c means so and and this Affinity
is proportional to the distance from the point to the center
of the cluster now then again Now we have the pros
and cons of fuzzy see means. So first of all, it allows a data point to be
in multiple cluster. That's a pro. It's a more neutral
representation of the behavior of jeans jeans usually are
involved in multiple functions. So it is a very
good type of clustering when we're talking
about genes First of and again, if we talk about the cons again, we have to Define c
which is the number of clusters same as K next. We need to determine the
membership cutoff value also, so that takes a lot of Time
and it's time-consuming and the Clusters are sensitive to initial
assignment of centroid. So a slight change or deviation from the center
has it's going to result in a very different
kind of, you know, a funny kind of output we get
from the fuzzy c means and one of the major disadvantage
of see means clustering is that it's this are
non deterministic algorithm. So it does not give you a particular output
as in such that's that now let's have a look. At the third type of clustering which is
the hierarchical clustering. So hierarchical clustering
is an alternative approach which builds a hierarchy
from the bottom up or the top to bottom and does not require
to specify the number of clusters beforehand. Now, the algorithm works
as in first of all, we put each data point
in its own cluster and if I the closest to Cluster and combine them into one more
cluster repeat the above step till the data points are
in a single cluster. Now, there are two types of
hierarchical clustering one is I've number 80 plus string and the other one
is division clustering. So a commemorative
clustering bills the dendogram from bottom level while the division clustering
it starts all the data points in one cluster
the fruit cluster now again hierarchical clustering also
has some sort of pros and cons. So in the pros
don't know Assumption of a particular number
of cluster is required and it may correspond
to meaningful taxonomist. Whereas if we talk
about the cons once a decision is made
to combine two clusters. Has it cannot be undone and one of the major disadvantage of
these hierarchical clustering is that it becomes very slow. If we talked about very very
large data sets and nowadays. I think every industry are using
last year as its and collecting large amounts of data. So hierarchical clustering
is not the act or the best method someone
might need to go for so there's that now when we talk
about unsupervised learning, so we have K means
clustering and again, Another important term which people usually Miss while
talking about us was running and there's one very
important concept of Market Basket analysis. Now, it is one
of the key techniques used by large retailers
to uncover association between items now
it works by looking for combination of items that occur together frequently in the transactions
to put it in other way. It allows retailers
to identify the relationships between the items that the People by
for example people who buy bread also tend to buy
butter the marketing team at the retail stores
should Target customers who buy bread and butter
and provide them and offer so that they buy
a third item like an egg. So if a customer buys bread
and butter and sees a discount or an offer on X, he will be encouraged to spend
more money and buy the eggs. Now, this is what Market Basket
analysis is all about now to find the association
between the two items and make predictions about
what the customers will buy. There are two Cartoons which are
the association rule Mining and the ebrary algorithms. So let's discuss each
of these algorithm with an example. First of all, if we have a look at
the association rule mining now, it's a technique that's shows how items are associated to
each other for example customers who purchased spread have
a 60 percent likelihood of also purchasing
jam and customers who purchase laptop are more
likely to purchase laptop bags. Now if you take an example
of an association rule if we have a look
at the Example here a arrow B. It means that if a person buys an atom a then
he will also buy an atom P. Now. There are three common ways to
measure a particular Association because we have to find
these rules not on the basis of some statistics, right? So what we do is use support confidence and lift
now these three common ways and the measures to have a look at the association rule
Mining and know exactly how good is that rule. So first of all, we have support So support
gifts the fraction of the Which contains an item A and B. So it's basically
the frequency of the item in the whole item set. Where's confidence gifts how often the item
A and B occurred together given the number
of item given the number of times a occur. So it's frequency
a comma B divided by the frequency of a now left what indicates is the strength
of the rule over the random co-occurrence of A and B. If you have a close look
at the denominator of the lift formula here, we have support a into support
be and now a major thing which can be noted from this is that the support of A
and B are independent here. So if the value of lift or the denominator value
of the lift is more it means that the items are independently
selling more not together. So that in turn will decrease
the value of lift. So what happens is that suppose the value
of lift is more that implies that the rule which we get. It implies that the rule
is strong and it And we used for later purposes
because in that case the support in to support P value, which is the denominator
of lift will be low which in turn means that there is a relationship
between the items in the and B. So let's take an example
of Association rule Mining and understand how
exactly it works. So let's suppose we have
a set of items a b c d and e and we have
the set of transactions which are T1 T2, T3, T4 and T5 and what we need to do is
create some sort of Rules, for example, you can see a d which means that
if a person buys a he buys D if a person by see he buys a
if a person buys a he by C. And for the fourth one is if a person by B
and C Hill in turn by a now what we need to do is calculate
the support confidence and lift of these rules now here again, we talked about
a priority algorithm. So a priori algorithm and the association rule mining
go hand in hand. So what a predator
This algorithm. It uses the frequent itemsets to
generate the association rules and it is based on the concept that a subset of a frequent itemsets must also
be a frequent Isom set. So let's understand what is a frequent item set and
how all of these work together. So if we take the following
transactions of items, we have transaction T
1 2 T 5 and the items are 1 3 4 2 3 5 1 2 3 5 2 5 and 1 3 5 now. Now another more
important thing about support which I forgot to mention was that when talking about Association rule mining
there is a minimum support count what we need to do. Now. The first step is
to build a list of items that of size 1 using
this transaction data set and use the minimum
support count to now, let's see how we do
that if we create the table see when you have a close look
at the table c 1 we have the items at one
which has support three because it appears
in the transaction one. Three and five similarly if you have a look at the item
set the single item 3. So it has the support
of for it appears in t 1 T 2 T 3 and T 5 but if we have a look at the item
set for it only appears in the transaction once so it's support value is
1 now the item set with the support value Which is less
than the minimum support value that is to have
to be eliminated. So the final table which is a table F1
has one two three. And five it does not
contain the for now. What we're going to do is
create the item list of the size 2 and all the combination
of the item sets in F1. I used in this iteration. So we're left for behind. We just have 1 2 3 & 5. So the possible item
sets a 1 2 1 3 1 5 2 3 2 5 & 3 5 then again. We will calculate the support So in this case if we have
a closer look at the table c 2 we see
that the items at once. What to do is having a support value 1
which has to be eliminated. So the final table f 2 does
not contain 1 comma 2 similarly if we create the item sets of size 3 and calculate
this support values, but before calculating
the support, let's perform the puring on the data set. Now what's appearing? So after all the combinations
are made we divide the table c 3 items to check if there are another subset
whose support is less than the minimum support value. This is a prairie algorithm. So in the item sets one, two, three what we can see
that we have one two, and in the one to five again, we have one too so build this
cardboard of these item sets and we'll be left
with 1 3 5 and 2 3 5. So with one three five, we have three subsets
one five one, three three five, which are present in table F2. Then again. We have two three
to five and 3/5 which are also present
in t will f 2 so we have 2 Move 1 comma
2 from the table c 3 and create the table F3 now if you're using the items of C3
to create the atoms of C-4. So what we find is that we have the item set
1 2 3 5 the support value is 1 Which is less than
the minimum support value of 2. So what we're going
to do is stop here and we're going to return
to the previous item set. That is the table
c 3 so the final table. Well, if three was
one three five with the support value of 2 and 2 3 5
with the support value of 2 now, what we're gonna do is
generate all the subsets of each frequent itemsets. So let's assume that minimum confidence value is
60% So for every subset s of I the output rule is
that s gives i2s is that s recommends i ns. If the support of I / support
of s is greater than or equal. Equal to the minimum
confidence value, then only will proceed further. So keep in mind that we have not used
left till now. We are only working
with support and confidence. So applying rules
with item sets of F3 we get rule 1 which is 1 comma
3 which gives 1 3 5 and 1/3. It means if you buy one
and three there's a 66% chance that you will buy item 5 also similarly the rule
1 comma 5 it means that If you buy one and five, there's a hundred percent chance that you will buy
three also similarly if we have a look
at Rule 5 and 6 here the confidence value is less than 60 percent which was
the assumed confidence value. So what we're going to do is
with reject these files now an important thing
to note here is that have a closer look
to the Rule 5 and root 3, you see it has one five three
one five three three point five. It's very confusing. So one thing to keep in Mine is that the order of the item sets
is also very important that will help us
allow create good rules and avoid any kind of confusion. So that's that. So now let's learn how Association rule I used in
Market Basket analysis problems. So what we'll do
is we will be using the online transactions data of a retail store for
generating Association rules. So first of all, what you need to do is
import pandas MSD ml. D&D libraries from the imported
and read the data. So first of all, what we're
going to do is read the data, what we're going
to do is from M LX T and E dot frequent patterns. We're going to improve the a
priori and Association rules. As you can see here. We have the head of the data. You can see we have invoice
number stock code the description quantity the invoice dt8 unit price
customer ID and the country. So in the next step, what we will do is we
will do the data cleanup which includes removing. His from some
of the descriptions given and what we're going
to do is drop the rules that do not have the invoice numbers every move
the crate transactions. So hey, what what you're
going to do is remove which do not have
any invoice number if the string tight ainst Epstein was a number then
we're going to remove that. Those are the credits remove
any kind of spaces from the descriptions. So as you can see here, we have like five hundred
and thirty-two thousand rows with eight columns. So next one. We're going to do is
after the cleanup. We need to consolidate the items
into one transaction per row with each product for the sake
of keeping the data set small. We're going to only look
at the sales for France. So we're going to use
the only France and group by invoice number description with the quantity sum
up and see so which leaves us
with three ninety two rows and one thousand five
hundred sixty three columns. Now, there are a lot
of zeros in the data, but we also need to make sure
Any positive values are converted to a 1 and
anything less than 0 is set to 0 so for that we're going
to use this code defining and code units if x is less than
0 return 0 if x is greater than 1 returned one. So what we're going to do is map and apply it to the whole data
set we have here. So now that we
have structured data properly. So the next step is to generate
the frequent item set that has support of at
least seven percent. Now this number is chosen so
that you can get close enough. Now, what we're going
to do is generate the rules with the corresponding
support confidence and lift. So we had given
the minimum support a 0.7. The metric is left
frequent Island set and threshold is 1 so these are the following rules now a few
rules with a high lift value, which means that it
occurs more frequently than would be expected
given the number of transaction the product combinations most
of the places the confidence. Is high as well. So these are few to observations
what we get here. If we filter the data frame
using the standard pandas code for large lift six
and high confidence 0.8. This is what the output
is going to look like. These are 1 2 3 4 5 6 7 8. So as you can see here, we have the H rules
which are the final rules which are given by
the Association rule Mining and this is how all
the industries are. Are any of these we've talked
about largely retailers. They tend to know how their products are used
and how exactly they should rearrange and provide
the offers on the product so that people spend
more and more money and time in the shop. So that was all
about Association rule mining. So so guys, that's all for
unsupervised learning. I hope you got to know
about the different formulas how unsupervised learning works
because you know, we did not provide
any label to the data. All we did was create some rules
and not knowing what the data is and we did clusterings
different types of clusterings came in simi's
hierarchical clustering. The reinforcement learning
is a part of machine learning where an agent is put
in an environment and he learns to behave
in this environment by performing certain actions. Okay, so it basically performs
actions and it either gets a rewards on the actions or It gets a punishment
and observing the reward which it gets from those actions
reinforcement learning is all about taking an appropriate
action in order to maximize the reward
in a particular situation. So guys in supervised learning
the training data comprises of the input and the expected output and so the model is trained
with the expected output itself, but when it comes
to reinforcement learning, there is no expected output here
the reinforcement agent decides. What actions to take in order
to perform a given task. In the absence of a training
data set it is bound to learn from its experience itself. All right. So reinforcement learning
is all about an agent who's put in
an unknown environment and he's going to use a hit
and trial method in order to figure out the environment and then come up
with an outcome. Okay. Now, let's look
at reinforcement learning within an analogy. So consider a scenario
where in a baby is learning how to walk the scenario
can go about in two ways. Now in the first case
the baby starts walking and makes it to the candy here. The candy is basically
the reward it's going to get so since the candy is the end goal. The baby is happy. It's positive. Okay, so the baby is happy
and it gets rewarded a set of candies now another way
in which this could go is that the baby starts walking but Falls due to some hurdle
in between the baby gets hurt and it doesn't get any candy
and obviously the baby is sad. So this is a negative reward. Okay, or you can say
this is a setback. So just like how we humans learn from our mistakes
by trial and error. Learning is also similar. Okay, so we have an agent which is basically
the baby and a reward which is the candy over here. Okay, and with many hurdles
in between the agent is supposed to find the best possible path
to read through the reward. So guys, I hope
you all are clear with the reinforcement learning. Now. Let's look at the
reinforcement learning process. So generally a reinforcement
learning system has two main components. All right, the first is an agent
and the second one is an environment now
in the previous case, we saw that the
agent was a baby. B and the environment
was the living room where in the baby was crawling. Okay. The environment is the setting that the agent is acting
on and the agent over here represents the reinforcement
learning algorithm. So guys the reinforcement
learning process starts when the environment
sends a state to the agent and then the agent
will take some actions based on the observations in turn the environment
will send the next state and the respective reward
back to the agent. The agent will update its knowledge with the reward
returned by the I meant and it uses that to evaluate
its previous action. So guys this
Loop keeps continuing until the environment sends
a terminal state which means that the agent has
accomplished all his tasks and he finally gets the reward. Okay. This is exactly what was depicted
in this scenario. So the agent keeps
climbing up ladders until he reaches his reward
to understand this better. Let's suppose that our agent is
learning to play Counter-Strike. Okay, so let's break it down
now initially the RL agent which is Only the player player
1 let's say it's the player 1 who is trying to learn
how to play the game. Okay. He collects some state
from the environment. Okay. This could be the first state
of Counter-Strike now based on the state the agent
will take some action. Okay, and this action
can be anything that causes a result. So if the player moves left or right it's also
considered as an action. Okay. So initially the action
is going to be random because obviously the first time
you pick up Counter-Strike, you're not going
to be a master at it. So you're going to try
with different actions and you're just going
to Up a random action in the beginning. Now the environment is going
to give a new state. So after clearing that the environment
is now going to give a new state to the agent or to the player. So maybe he's
across stage 1 now. He's in stage 2. So now the player will get a reward
our one from the environment because it cleared stage 1. So this reward can be anything. It can be additional points
or coins or anything like that. Okay. So basically this Loop
keeps going on until the player is dead
or reaches the destination. Okay, and it Continuously
outputs a sequence of States actions and rewards. So guys. This was a small example to show you how reinforcement
learning process works. So you start
with an initial State and once a player clothes
that state he gets a reward after that the environment will
give another stage to the player and after it clears that state
it's going to get another reward and it's going to keep happening until the player
reaches his destination. All right, so guys,
I hope this is clear now, let's move on and look at the reinforcement
learning definition. So there are a few Concepts
that you should be aware of while studying
reinforcement learning. Let's look at those
definitions over here. So first we have the agent
now an agent is basically the reinforcement learning
algorithm that learns from trial and error. Okay. So an agent takes actions, like for example a soldier
in Counter-Strike navigating through the game. That's also an action. Okay, if he moves left right
or if he shoots at somebody that's also an action. Okay. So the agent is responsible for taking actions
in the environment. Now the environment is
the whole Counter-Strike game. Okay. It's basically the world
through which the agent moves the environment takes
the agents current state and action as input and it Returns the agency reward
and its next state as output. Alright, next we have action
now all the possible steps that an agent can take
are called actions. So like I said, it can be moving right left
or shooting or any of that. Alright, then we have
state now state is basically the current condition
returned by the environment. So Double State you are in if you are in state 1 or
if you're interested to that represents
your current condition. All right. Next we have reward a reward
is basically an instant return from the environment
to appraise Your Last Action. Okay, so it can be
anything like coins or it can be additional points. So basically a reward
is given to an agent after it clears. The specific stages. Next we have policy policy is
basically the strategy that the agent uses to find
out his next action. In based on his current
state policy is just the strategy with which
you approach the game. Then we have value. Now while you is the expected
long-term return with discount so value and action value
can be a little bit confusing for you right now. But as we move further, you'll understand what
I'm talking about. Okay, so value is basically
the long-term return that you get with discount. Okay discount, I'll explain
in the further slides. Then we have action value now action value
is also known as Q value. Okay, it's very similar
to what You except that it takes
an extra parameter, which is the current action. So basically here you'll find
out the Q value depending on the particular action
that you took. All right. So guys don't get confused
with value and action value. We look at examples in the further slides and you
will understand this better. Okay, so guys make sure that you're familiar
with these terms because you'll be seeing
a lot of these terms in the further slides. All right. Now before we move any further, I'd like to discuss
a few more Concepts. Okay. So first we will discuss
the reward maximization. So if you haven't already
realize the it the basic aim of the RL agent is
to maximize the reward now, how does that happen? Let's try to understand
this in depth. So the agent must be
trained in such a way that he takes the best action
so that the reward is maximum because the end goal
of reinforcement learning is to maximize your reward
based on a set of actions. So let me explain this
with a small game now in the figure you can see there
is a Forks there's some meat and there's a tiger So
odd agent is basically the fox and his end goal is to eat
the maximum amount of meat before being eaten
by the tiger now since the fox is a clever fellow he eats the meat that is closer to him
rather than the meat which is closer to the tiger. Now this is because the
closer he is to the tiger the higher are his chances
of getting killed. So because of this the rewards
which are near the tiger, even if they are
bigger meat chunks, they will be discounted. So this is exactly
what discounting means so our agent is not going
to eat the meat chunks which are Closer to the tiger
because of the risk. All right now even though the meat chunks
might be larger. He does not want to take
the chances of getting killed. Okay. This is called discounting. Okay. This is where you discount because it improvised
and you just eat the meat which are closer to you
instead of taking risks and eating the meat which are closer
to your opponent. All right. Now the discounting
of reward Works based on a value called gamma
will be discussing gamma in our further slides, but in short the value
of gamma is between 0 and 1. Okay. So the Follow the gamma. The larger is
the discount value. Okay. So if the gamma value is lesser, it means that the agent
is not going to explore and he's not going
to try and eat the meat chunks which are closer to the tiger. Okay, but if the gamma value
is closer to 1 it means that our agent is actually
going to explore and it's going to dry
and eat the meat chunks which are closer to the tiger. All right now, I'll be explaining this
in depth in the further slides. So don't worry if you haven't got
a clear concept yet, but just understand
that reward maximized. Ation is a very important step when it comes
to reinforcement learning because the agent has
to collect maximum rewards by the end of the game. All right. Now, let's look
at another concept which is called exploration
and exploitation. So exploration like the name
suggests is about exploring and capturing more information
about an environment on the other hand exploitation
is about using the already known exploited information
to hide in the rewards. So guys consider the fox
and tiger example that we discussed now here the foxy Only the meat chunks
which are close to him, but he does not eat
the meat chunks which are closer to the tiger. Okay, even though they
might give him more Awards. He does not eat them if the fox only focuses
on the closest rewards, he will never reach
the big chunks of meat. Okay, this is
what exploitation is about you just going to use
the currently known information and you're going
to try and get rewards based on that information. But if the fox decides
to explore a bit, it can find the bigger award
which is the big chunks of meat. This is exactly
what exploration is. So the agent is not going
to stick to one corner instead. He's going to explore
the entire environment and try and collect bigger rewards. All right, so guys, I hope you all are clear with
exploration and exploitation. Now, let's look
at the markers decision process. So guys, this is basically
a mathematical approach for mapping a solution in
reinforcement learning in a way. The purpose of reinforcement
learning is to solve a Markov decision process. Okay, so there are
a few parameters. Was that I used to get
to the solution. So the parameters include
the set of actions the set of states the rewards the policy that you're taking to approach
the problem and the value that you get. Okay, so to sum it up
the agent must take an action a to transition from a start state
to the end State s while doing so the agent
will receive a reward are for each action that he takes. So guys a series of actions taken by
the agent Define the policy or a defines the approach. And the rewards that are collected
Define the value. So the main goal here is
to maximize the rewards by choosing the optimum policy. All right. Now, let's try to understand
this with the help of the shortest path problem. I'm sure a lot of you might
have gone through this problem when you are in college, so guys look
at the graph over here. So our aim here is
to find the shortest path between a and d
with minimum possible cost. So the value that you see
on each of these edges basically denotes the cost. So if I want to go from A to see
it's gonna cost me 15 points. Okay. So let's look at
how this is done. Now before we move
and look at the problem in this problem the set of
states are denoted by the nodes, which is ABCD and the action is to Traverse
from one node to the other. So if I'm going from A to B, that's an action
similarly a to see that's an action. Okay, the reward is
basically the cost which is represented
by each Edge over here. All right. Now the policy is basically
the path that I choose to reach the destination, so Let's say I choose
a seed be okay, that's one policy
in order to get to D and choosing a CD
which is a policy. Okay. It's basically how
I'm approaching the problem. So guys here you
can start off at node a and you can take baby steps
to your destination. Now initially you're clueless so you can just take
the next possible node, which is visible to you. So guys, if you're smart enough, you're going to choose a
to see instead of ABCD or ABD. All right. So now if you are at nodes
see you want to drive. String note D. You must again
choose a weisbarth. All right, you just have
to calculate which path has the highest cost or which path will give
you the maximum rewards. So guys, this is
a simple problem. We just trying to calculate
the shortest path between a and d by traversing
through these nodes. So if I Traverse from a CD,
it gives me the maximum reward. Okay, it gives me 65, which is more than any other
policy would give me. Okay. So if I go from ABD, it would be 40 when you
compare this to a CD. It gives me more reward. So obviously I'm going
to go with a CB. Okay, so guys was
a simple problem in order to understand how
Markov decision process works. All right, so guys,
I want to ask you a question. What do you think? I did hear did I perform
exploration or did I perform exploitation now the policy for the above example
is of exploitation because we didn't explore
the other nodes. Okay. We just selected three notes
and we travel through them. So that's why this
is called exploitation. We must always explore
the different notes so that we Find
a more optimal policy. But in this case, obviously a CD has
the highest reward and we're going with a CD but
generally it's not so simple. There are a lot of nodes there
hundreds of notes you Traverse and there are like
50 60 policies. Okay, 50 60 different policies. So you make sure you explore
through all the policies and then decide
on an Optimum policy which will give you
a maximum reward the for a robot and environment is a place where It has been
put to use now. Remember this reward is itself the agent for example
an automobile Factory where a robot is used
to move materials from one place to another now
the task we discussed just now have a property in common. Now, these tasks involve
and environment and expect the agent to learn
from the environment. Now, this is where traditional
machine learning phase and hence the need
for reinforcement learning now. It is good to have
an established overview of the problem. That is to be Of using
the Q learning or the reinforcement learning so it helps to define
the main components of a reinforcement
learning solution. That is the agent environment
action rewards and States. So let's suppose we are to build a few autonomous robots for
an automobile building Factory. Now, these robots will help
the factory personal by conveying them
the necessary parts that they would need
in order to pull the car. Now. These different parts
are located at nine different positions
within the factory warehouse. The car part include
the chassis Wheels dashboard the engine and so on and the factory workers
have prioritized the location that contains the body or the chassis to be
the topmost but they have provided the priorities
for other locations as well, which will look into the moment. Now these locations within the factory look
somewhat like this. So as you can see here, we have L1 L2 L3 all of these stations now
one thing you might notice here that there Little obstacle
prison in between the locations. So L6 is the top
priority location that contains the chassis
for preparing the car bodies. Now the task is
to enable the robots so that they can find
the shortest route from any given location to
another location on their own. Now the agents in this case
are the robots the environment is the automobile
Factory warehouse. So let's talk about the state's
the states are the location in which a particular robot is And in the particular
instance of time, which will denote it states
the machines understand numbers rather than let us so let's map
the location codes to number. So as you can see here, we have mapped location
l 1 to this t 0 L 2 and 1 and so on we have L8 as
state 7 and L line at state. So next what we're going to talk
about are the actions. So in our example, the action will be
the direct location that a robot can go from a particular location
right considering What that is a tel to location and the Direct locations to
which it can move rl5 L1 and L3. Now the figure here may come
in handy to visualize this now as you might have already
guessed the set of actions here is nothing but the set of all possible states of the robot for each location
the set of actions that a robot can take
will be different. For example, the set
of actions will change if the robot is
in L1 rather than L2. So if the robot is Is
in L1 it can only go to L 4 and L 2 directly now that we are done with the states
and the actions. Let's talk about the rewards. So the states are
basically zero one two, three four and the
actions are also 0 1 2 3 4 up to 8. Now. The rewards now will
be given to a robot. If a location which is the state
is directly reachable from a particular location. So let's take an example suppose
L line is directly reachable from L8, right? If a robot goes from LA
to align and vice versa, it will be rewarded by one and if I look a shin is
not directly reachable from a particular equation. We do not give any reward
a reward of 0 now the reward is just a number and nothing else it enables
the robots to make sense of the movements helping them in deciding what locations
are directly reachable and what are not now with this Q. We can
construct a reward table which contains all
the required values mapping between all possible States. So as you can see here
in the table the positions which are marked green
have a positive reward. And as you can see here, we have all the possible rewards
that a robot can get by moving in between the different states. Now comes an
interesting decision. Now remember that the factory
administrator prioritized L6 to be the topmost. So how do we incorporate
this fact in the above table. Now, this is done by associating
the topmost priority location with a very high reward
than the usual ones. So let's put 990. And in the cell L 6 comma and 6 now the table of rewards
with a higher reward for the topmost location
looks something like this. We have not formally defined
all the vital components for the solution. We are aiming for
the problem discussed. Now, you will shift gears
a bit and study some of the fundamental concepts that Prevail in the world
of reinforcement learning and q-learning the first
of all we'll start with the Bellman
equation now consider the following Square rooms, which is analogous
to the actual environment. Aunt from our original problem, but without the barriers now
suppose a robot needs to go to the room marked in the green promise
current position a using the specified Direction now, how can we enable the robot
to do this programmatically one idea would be introduced
some kind of a footprint which the robot will be able
to follow now here a constant value is specified
in each of the rooms which will come
along the robots way if it follows the direction
specified above now in this way if it starts at A it
will be able to scan through this constant value and will move accordingly
but this will only work if the direction is prefix and the robot always starts at the location a now
consider the robot starts at this location rather
than its previous one. Now the robot
now sees Footprints in two different directions. It is therefore unable
to decide which way to go in order to get the destination
which is the Green Room. It happens primarily because the robot
does not have a weight. Remember the directions
to proceed so our job now is to enable
the robot with a memory. Now, this is where the Bellman
equation comes into play. So as you can see here, the main reason
of the Bellman equation is to enable the reward
with the memory. That's the thing
we're going to use. So the equation goes
something like this V of s gives maximum a r
of s comma a plus gamma of vs - where s is a particular state which is a ROM a is
the Action Moving between the rooms as - is the state to which
the robot goes from s and gamma is the discount Factor now we'll get
into it in a moment and obviously R of s comma
a is a reward function which takes a state as an action
a and outputs the reward now V of s is the value of being
in a particular state which is the footprint now we consider all
the possible actions and take the one that yields
the maximum value now, there is one constraint however
regarding the value Footprint, that is the row marked in the yellow just
below the Green Room. It will always have
the value of 1 to denote that is one of the nearest room adjacent to the Green Room
not this is also to ensure that a robot gets a reward when it goes from a yellow room
to The Green Room. Let's see how to make
sense of the equation which we have here. So let's assume
a discount factor of 0.9 as remember gamma is
the discount value or the discount Factor. So let's take a 0.9
now for the room, which is Just below the one or the yellow room, which is
the Aztec Mark for this room. What will be the V of s that is the value of being
in a particular state? So for this V of s
would be something like maximum of a will take 0 which is the initial
of our s comma. Hey plus 0.9
which is gamma into 1 that gives us zero point
nine now here the robot will not get any reward for going to a state
marked in yellow. Hence the ER s comma a is 0 here but the robot knows the value
of being in the yellow room. Hence V of s Dash is
one following this for the other states. We should get 0.9 then again, if we put 0.9 in this equation, we get 0.81 than 0.7 to 9
and then we again reach the starting point. So this is how the table looks with
some value Footprints computed from the Bellman equation now
a couple of things to It is here is that the max function
has the robot to always choose the state that gives it the maximum value
of being in that state. Now the discount Factor
gamma notifies the robot about how far it is
from the destination. This is typically specified by
the developer of the algorithm. That would be installed
in the robot. Now, the other states can also
be given their respective values in a similar way. So as you can see here
the boxes adjacent to the green one have one and if we Move away from 1 we
get 0.9 0.8 1 0 1 7 to 9 and finally we reach 0.66. Now the robot now
can precede its way through the Green Room utilizing
these value Footprints event if it's dropped
at any arbitrary room in the given location now, if a robot Lance up in
the highlighted Sky Blue Area, it will still find
two options to choose from but eventually either
of the parts will be good enough for the robot to take because Auto V
the value for prints and only that out. Now one thing to note is that the Bellman equation is one
of the key equations in the world of reinforcement
learning and Q learning. So if we think realistically our
surroundings do not always work in the way we expect
there is always a bit of stochastic City
involved in it. So this applies
to robot as well. Sometimes it might so happen that the robots
Machinery got corrupted. Sometimes the robot may come
across some hindrance on its way which it
may not be known to it beforehand. Right and sometimes even
if the robot knows that it needs to take
the right turn it will not so how do we introduce
this to cast a city in our case now here comes
the Markov decision process. So consider the robot is
currently in the Red Room and it needs to go
to the green room. Now. Let's now consider
the robot has a slight chance of dysfunctioning and might take
the left or the right or the bottom turn instead
of digging the upper turn and are Get to the Green Room
from where it is now, which is the Retro. Now the question is, how do we enable the robot
to handle this when it is out in the given environment right. Now, this is a situation where the decision making regarding which turn is
to be taken is partly random and partly another control
of the robot now partly random because we are not sure when exactly the robot mind
dysfunctional and partly under the control of the robot because it is still making a decision of taking
a turn right on its own. And with the help
of the program embedded into it. So a Markov decision process is a discrete time
stochastic Control process. It provides a mathematical
framework for modeling decision-making in situations where the outcomes
are partly random and partly under the control
of the decision maker. Now we need to give this concept a mathematical shape
most likely an equation which then can be taken further. Now you might be surprised that we can do this with the
help of the Bellman equation. Action with a few minor tweaks. So if we have a look
at the original Bellman equation V of X is equal to maximum of our s comma
a plus gamma V of s - what needs to be changed
in the above equation so that we can introduce
some amount of Randomness here as long as we are not sure when the robot might not take
the expected turn. We are then also not sure
in which room it might end up in which is nothing but the ROM it moves from its current room
at this point according. To the equation. We are not sure of the a stash which is the next state
or the room, but we do know all the probable
turns the robot might take now in order to incorporate each of this probabilities
into the above equation. We need to associate
a probability with each of the turns to
quantify the robot. If it has got any expertise
chance of taking the stern know if we do so we get
PS is equal to maximum of RS comma a plus gamma
into summation of s - PS comma a comma s stash into V
of his stash now the PS a-- and a stash is the probability of moving from room s
to establish with the action a and the submission
here is the expectation of the situation. That's a robot in curse, which is the randomness now,
let's take a look at this example here. So when we associate the probabilities to each
of these terms Owns, we essentially mean
that there is an 80% chance that the robot will
take the upper turn. Now, if you put all
the required values in our equation, we get V of s is equal
to maximum of R of s comma a + comma of 0.8 into V of room up plus zero point 1 into V
of room down 0.03 into Rome of V of from left plus 0.03
into V of room right now note that the value footprints. Not change due to the fact that we are incorporating
stochastically here. But this time we
will not calculate those values Footprints instead. We will let the robot
to figure it out. Now up until this point. We have not considered
about rewarding the robot for its action of going
into a particular room. We are only watering the robot when it gets
to the destination now, ideally there should be a reward
for each action the robot takes to help it better assess
the quality of the actions, but the there was need
not to be always be the same but it is much better
than having some amount of reward for the actions
than having no rewards at all. Right and this idea is known as
the living penalty in reality. The reward system
can be very complex and particularly modeling
sparse rewards is an active area of research in the domain
of reinforcement learning. So by now we have got
the equation which we have a so what we're going to do is
now transition to Q learning. So this equation gives
us the value of going to a particular State
taking the stochastic city of the environment into account. Now, we have also learned
very briefly about the idea of living penalty which deals with associating
each move of the robot with a reward. So Q learning processes and idea of assessing
the quality of an action that is taken to move to
a state rather than determining the possible value of the state which is being moved
to so earlier. We had 0.8 into V. E
of s 1 0.03 into V of S 2 0 point 1 into V
of S 3 and so on now if you incorporate the idea
of assessing the quality of the action for moving
to a certain state so the environment
with the agent and the quality of the action
will look something like this. So instead of 0.8 V of s 1 will have q of s
1 comma a one will have q of S 2 comma 2 Q of S 3 now
the robot now has food. In states to choose from
and along with that there are four different actions also for
the current state it is in so how do we calculate Q of s comma a that is the cumulative quality
of the possible actions the robot might take so
let's break it down. Now from the equation V of s
equals maximum a RS comma a + comma summation s -
PSAs - into V of s - if we discard them. Maximum function we have is
of a plus gamma into summation p and v now essentially
in the equation that produces V of s. We are considering
all possible actions and all possible States
from the current state that the robot is in and then we are taking the maximum value caused
by taking a certain action and the equation produces
a value footprint, which is for just
one possible action. In fact, we can think
of it as the quality of the So Q of s comma a
is equal to RS comma a + comma of summation p and v now that we have got an equation
to quantify the quality of a particular action. We are going to make
a little adjustment in the equation we can now say that V of s is the maximum
of all the possible values of Q of s comma a right. So let's utilize this fact and replace V of s Dash as
a function of Q. So Q U.s. Comma a becomes R of s comma a +
comma of summation PSAs - and maximum of the que es - a - so the equation of V is now
turned into an equation of Q, which is the quality. But why would we do that now? This is done to
ease our calculations because now we have
only one function Q which is also the core of the
dynamic programming language. We have only one. Ocean Q to calculate and R of s comma a is
a Quantified metric which produces reward
of moving to a certain State. Now, the qualities of the actions are
called The Q values and from now on we will refer
to the value Footprints as the Q values
an important piece of the puzzle is
the temporal difference. Now temporal difference
is the component that will help the robot
calculate the Q values which respect to the changes
in the environment over time. So consider Our robot is
currently in the mark State and it wants to move
to the Upper State. One thing to note that here is that the robot already knows
the Q value of making the action that is moving through
the Upper State and we know that the environment
is stochastic in nature and the reward that the robot will get
after moving to the Upper State might be different
from an earlier observation. So how do we capture
this change the real difference? We calculate the new q s comma a
with the same formula and subtract the Previously
known qsa from it. So this will in turn
give us the new QA. Now the equation that we just derived gifts
the temporal difference in the Q values which further helps
to capture the random changes in the environment which may impose now
the name q s comma a is updated as the following
so Q T of s comma is equal to QT minus 1 s comma a plus Alpha D DT of a comma s now here Allah Alpha is
the learning rate which controls how quickly the robot adapts
to the random changes imposed by the environment the qts comma
is the current state q value and a QT minus 1 s comma is
the previously recorded Q value. So if we replace the TDS comma a
with its full form equation, we should get Q T of s
comma is equal to QT - 1 of s comma y plus Alpha into R of s
comma a plus gamma maximum. Q s Dash a dash minus QT minus 1 s comma a now that we have all the little
pieces of q line together. Let's move forward
to its implementation part. Now, this is the final equation
of q-learning, right? So, let's see how we can implement this
and obtain the best path for any robot to take now
to implement the algorithm. We need to understand
the warehouse location and how that can be mapped
to different states. So let's start by reconnecting
the sample environment. So as you can see here, we have L1 L2 L3 to align
and as you can see here, we have certain borders also. So first of all, let's map each of the above
locations in the warehouse two numbers or the states so that it will ease
our calculations, right? So what I'm going to do is
create a new Python 3 file in the jupyter notebook
and I'll name it as q-learning. Number. Okay. So let's define the states. But before that what we
need to do is import numpy because we're going to use numpy for this purpose and let's
initialize the parameters. That is the gamma
and Alpha parameters. So gamma is 0.75 which is the discount Factor
whereas Alpha is 0.9, which is the learning rate. Now next what we're going to do
is Define the states and map it to numbers. So as I mentioned Earlier l
1 is 0 and Dylan line. We have defined the states
in the numerical form. Now. The next step is to define
the actions which is as mentioned above
represents the transition to the next state. So as you can see here, we have an array
of actions from 0 to 8. Now, what we're going to do
is Define the reward table. So as you can see,
it's the same Matrix that we created just now that I showed you just now now
if you understood it correctly, there isn't any real
Barrel limitation as depicted in the image, for example, the transitional
for tell one is allowed but the reward will be
zero to discourage that path or in tough situation. What we do is add
a minus 1 there so that it gets
a negative reward. So in the above code snippet
as you can see here. I took each of the states and
put once in the respective state that are directly reachable
from the certain State now, if you refer to that reward
table, once again, what we created the above, our reconstruction will
be easy to understand but one thing to note here is that we did not consider the top
priority location L6 yet. We would also need
an inverse mapping from the state's back
to its original location and it will be cleaner when we reach to the utter
depths of the algorithms. So for that what we're going
to do Is have the inverse map location State delegation. We will take the distinct
State and location and convert it back. Now. What we'll do is we will now
Define a function get optimal which is the get optimal route, which will have a start location
and an N location. Don't worry. The code is pick but I'll explain you each
and every bit of the code. Now the get optimal
route function will take two arguments the style location
in the warehouse and the end location
in the warehouse recipe lovely and it will return
the optimal route for reaching the end location from the starting location
in the form of an ordered list containing the letters. So we'll start by defining the function by initializing
the Q values to be all zeros. So as you can see here, we have given the Q value
has to be 0 but For that what we need to do is copy
the reward Matrix to a new one. So this is the rewards
new and next again. What we need to do is get
the ending State corresponding to the ending location. And with this information
automatically will set the priority of the given ending
stay to the highest one that we are not defining it now, but will automatically
set the priority of the given ending
State as nine nine nine. So what we're going
to do is initialize the Q values to be 0 and
in the queue learning process what you can see See here. We are taking I in range
1,000 and we're going to pick up a state randomly. So we're going to use
the MP dot random r + NT and for traversing
through the neighbor location in the same maze. We're going to iterate
through the new reward Matrix and get the actions which are greater
than 0 and after that what we're going to do is pick
an action randomly from the list of the playable actions in years to the next state will going to compute
the temporal difference, which is TD, which is the rewards plus gamma
into The queue of next state and will take n p dot ARG Max of Q of next eight minus Q
of the current state. We going to then update the Q values using
the Bellman equation as you can see here, you have the Bellman equation and we're going
to update the Q values and after that we're going
to initialize the optimal route with a starting location
now here we do not know what the next location yet. So initialize it with the value
of the starting location, which again is the random Shh
now we do not know about the exact number
of iteration needed to reach to the final location. Hence while loop will be
a good choice for the iteration. So when you're going to fetch
the starting State fetch the highest Q value penetrating to the starting State
we go to the index or the next state, but we need
the corresponding letter. So we're going to use that state
to location function. We just mentioned there and after that we're going
to update the starting location for the next iteration. Finally, we'll return the root. So let's take the starting
location of n line and and location of L1 and see
what part do we actually get? So as you can see here, we get Airline l8l
five L2 and L1. And if you have a look
at the image here, we have if we start from L9 to L1 we got l8l 5 L
2 l 1 L HL 5 L2 L1. That would yield us the maximum. Mm value of the maximum
reward for the robot. So now we have come to the end of this Q learning session
the past year has seen a lot of great examples
for machine learning and many new high-impact application of machine
learning with discovered and brought to light especially
in the healthcare Finance the speech recognition
augmented reality and much more complex 3D
and video applications. The natural language
processing was easily the most talked about domain within the community
with the likes of you. Lmf it and but
being open sourced. So let's have a look at some of the amazing
machine learning projects which are open sourced
the code is available for you. And those are discussed in
this 2018 to nine in Spectrum. So the first and the foremost
is tensorflow dot DS now machine learning in the browser or fictional thought
a few years back. Back and a stunning reality. Now a lot of us in this field
are welded to our favorite IDE, but tells of not DOT JS has the
potential to change your habits. It's become a very popular
released since its release earlier this year and continues to amaze
with its flexibility. Now as a repository states, there are primarily
three major features of terms of rho dot J's
develop machine learning and deep learning models in your process
itself run pre-existent as a flow models within
the browser retrain our Gene these prediction models as well. And if you are familiar with
Kara's the high-level layers EPA will seem quite familiar, but there are plenty of examples
available on GitHub repository. So do check out those legs
to Quicken your learning curve. And as I mentioned earlier, I'll leave the links
to all of these open source machine learning projects
in the description below. The next what we
not discuss is detector on it is developed by Facebook
and made a huge Splash when it was earlier launched in. An 80 those developed by
Facebook's AI research team, which is fa ir. And it implements the state of the art object
detection frame was it is written in Python and as help enable
multiple projects including the dance pose. Now, we'll know what exactly is then suppose
after this example and this repository
contains the code of over 70 preacher involves. So it's a very good
open source small guys. So to check it out now
the moment I talked about then suppose. That's the next one. I'm going to talk about so That's supposed stents human
pose estimation in the wild, but the code to train
and evaluate your own dance pose using the our CNN model
is included here and I've given the link
to the open source code in the description below and there are notebooks
available as well to visualize certain Sports cocoa data set
the next on our list. We have D
painterly harmonization. Now, I want you to take
a moment to just admire the above images. Can you tell which ones
we're done by a human and which one by a machine? I certainly could not now here. The first frame is
the input image the original one and a third frame as you can see here
has been generated by this technique amazing, right? The algorithm has
an external object to your choosing to any image and manages it to make it look
like nothing touched it now, make sure you check out
the code and try to implement it on different sets
of images yourself. It is really really fun. But talking about images. We have image out painting now what if I give you an image and
ask you to extend Its boundaries by imagining what it would look like when the entire
scene was captured. You would understandably turn
to some image editing software. But here's the awesome news. You can achieve it
in few lines of code, which is the image out painting. Now. This project is Akira's
implementation of Stanford image out failing paper, which is incredibly cool
and Illustrated paper. And this is how most
research paper should be. I've given the links
in the description below to check it out
guys and see how you can. Implement it now. Let's talk about audio
processing which is an another field where machine learning
has started to make its mark. It is not just limited
to generate music. You can do tasks like audio
classification fingerprinting segmentation tagging and much
more and there is a lot that's still yet to be explored and who knows perhaps you
could use this project to Pioneer your way to the top. Now what if you want
to discover your own planner now that might perhaps
be overstating things a bit, but the astronaut repository
will definitely get you close. The Google brain team discovered two new planets in the summer
2017 by applying the astronaut. It's a deep neural network
meant for working with astronomical data. It goes to show
the far-ranging application of machine learning and was
a truly Monumental development. And now the team behind the technology has
open source the entire code, so go ahead and check
out your own planet and who knows you might even have
a planet on your name now, I could not possibly
let this section. Pass by without
mentioning the brt. The Google AI is released
has smashed record on his way to winning the hearts
of NLP enthusiasts and experts alike following you. Lmf it and he LMO brt really
blew away the competition with its performance. It obtained a state
of art result on 11 and LP task apart from
the official Google repository. There is a python
implementation of birth, which is worth checking out
whether it makes a new era or not in natural
language processing. The thing we will soon
find out now add on it. I'm sure you guys
might have heard of it. It is a framework
for automatically learning high quality models without
requiring programming expertise since it's a Google invention. The framework is based
on tensorflow and you can build and simple models
using a Danette and even extend it to use
to train a neural network. Now the GitHub page contains
the code and example the API documentation and other things to get
your hands dirty the trust me Otto ml is the next big thing. NG in our field now if you follow a few researchers
on social media, you must have come
across some of the images. I am showing here in a video form a stick human
running across the terrain or trying to stand
up or some sort, but that my friends
is reinforcement learning and action now, here's a signature example
of it a framework to create a simulated humanoid to imitate
multiple motion skin. So let's have a look
at the top 10 skills. Are required to become a successful machine
learning engineer. So starting with
programming languages python is the lingua Franca
of machine learning. You may have had
exposure to buy them. Even if you weren't previously in programming or in a computer
science research field. However, it is important to have
a solid understanding of glasses and data structures. Sometimes python won't
be enough often. You'll encounter projects that need to leverage hardware
for Speed improvements. Now, make sure you are familiar
with the basic algorithms as well as the classes. Memory management
and linking now if you want a job
in machine learning, you will probably have
to learn all of these languages at some point C++ can help
in speeding code up. Whereas our works great
in statistics and plots and Hadoop is java-based. So you probably need
to implement mappers and reducers in Java. Now next we have linear algebra. You need to be intimately
familiar with mattresses vectors and matrix multiplication if you have an understanding
of derivatives and integrals, You should be in the clear. Otherwise even simple concept like gradient descent
will elude you statistic is going to come up a lot at
least make sure you are familiar with the caution distributions
means standard deviation and much more every bit
of statistical understanding Beyond this helps
the theories help in learning about algorithms great
samples are naive buys gaussian mixture models
and hidden Markov models. You need to have a firm
understanding of probability and stats to understand
these these models just go nuts and study measure Theory and next we have advanced
signal processing techniques. Now feature extraction is one
of the most important parts of machine learning
different types of problems need various Solutions. You may be able to utilize
really cool Advanced signal processing algorithms
such as wavelets share. Let's go blades
and bandless you need to learn about the time-frequency
analysis and try to apply it in your problems. Now, this skill will give
you an edge over all the other skills not this kid. Will give you an edge while you're applying for a machine learning engine
the job or others or next we have applied maths a lot of machine
learning techniques out. There are just fancy types
of functional approximation. Now these often get developed
by theoretical mathematician and then get applied by people who do not understand
the theory at all. Now the result is that many developers
might have a hard time finding the best techniques
for the problem. So even a basic understanding
of numerical analysis will give you a huge Edge having
a firm understanding. Ending of algorithm
Theory and knowing how the algorithm works. You can also discriminate models
such as svm's now you will need to understand subjects such as gradient descent convex
optimization LaGrange quadratic programming partial
differentiation equations and much more now all this math
might seem intimidating at first if you have been away
from it for a while just machine learning is
much more math intensive than something like
front-end developer. Just like any other skill
getting better at math is a man. Our Focus practice
the next skill in our list is the neural
network architectures. We need machine learning
for tasks that are too complex for human to quote
directly that is tasks that are so complex that it is Impractical now
neural networks are a class of models within the general
machine learning literature or neural networks are
a specific set of algorithms that have revolutionized
machine learning. They're inspired by
biological neural networks, and the current so-called
deep neural networks have proven to work quite well. Well, the neural
networks are themselves General function approximations, which is why they can be applied to almost
any machine learning problem about learning a complex mapping from the input
to the output space. Of course, there are still
good reason for the surge in the popularity
of neural networks, but neural networks have been
by far the most accurate way of approaching many problems like
translation speech recognition and image classification now
coming to our next point which is the natural
language processing now since it combines
computer science and Listed, there are a bunch of libraries
like the NLT K chances. Mm and the techniques
such as sentimental analysis and summarization that are unique to NLP now audio
and video processing has a frequent overlap with
the natural language processing. However, natural language
processing can be applied to non audio data
like text voice and audio analysis involves
extracting useful information from the audio signals
themselves being well-versed in math will get
you far in this one and you should also be familiar. Her with the concepts such as
the fast Fourier transforms. Now, these were
the technical skills that are required to become a successful
machine learning engineer. So next I'm going to discuss
some of the non-technical skills or the soft skills, which are required to become
a machine-learning engineer. So first of all,
we have the industry knowledge. Now the most successful
machine learning projects out. There are going to be those that address real pain points
whichever industry we are working for you should know how that industry works and Will be beneficial
for the business if a machine learning engineer
does not have business Acumen and the know-how of the elements that make up
a successful business model or any particular algorithm. Then all those technical skills
cannot be Channel productively, you won't be able
to discern the problems and potential challenges
that need solving for the business to sustain and grow you won't
really be able to help your organization explore
new business opportunities. So this is a must-have
skill now next we have effective communication. You'll need to explain
the machine learning Concepts to the people
with little to no expertise in the field chances
are you'll need to work with a team of Engineers as
well as many other teams. So communication is going
to make all of this much more easier companies searching for a strong machine learning
engineer looking for someone who can clearly and fluently translate
their technical findings to a non technical team
such as marketing or sales department
and next on our list. We have rapid prototyping so
Iterating on ideas as quickly as possible is mandatory
for finding one that works in machine learning
this applies to everything from picking up the right model to working on projects
such as A/B Testing you need to do a group of techniques used to quickly
fabricate a scale model of a physical part or assembly using the three-dimensional
computer aided design, which is the cat so last but not the least we
have the final skill and that is to keep updated. You must stay up to date
with Any upcoming changes every month new neural
network models come out that are performed
the previous architecture. It also means being aware of the news regarding
the development of the tools the changelog the conferences and much more you need to know about
the theories and algorithms. Now this you can achieve by reading the research papers
blogs the conference's videos. And also you need to focus
on the online community with changes very quickly. So expect and cultivate
this change now, this is not the Here we have
certain skills the bonus skills, which will give you an edge
over other competitors or the other persons who are applying for a machine-learning engineer
position on the bonus point. We have physics. Now, you might be in a situation where you're like to apply
machine learning techniques to A system that will interact with the real
world having some knowledge of physics will take
you far the next we have reinforcement learning. So this reinforcement learning
has been a driver behind many of the most
exciting developments in the Deep learning and the AI community. T from the alphago zero to
the open a is Dota 2 pot. This will be
a critical to understand if you want to go
into robotics self-driving cars or other AI related areas. And finally we have computer vision out of all
the disciplines out there. There are by far the most resources available
for learning computer vision. This field appears to have
the lowest barriers to entry but of course this likely means you will face
slightly more competition. So having a good knowledge
of computer vision how it rolls will
give you an edge. Other competitors now. I hope you got acquainted
with all the skills which are required
to become a successful machine learning engineer. As you know, we are living in the worlds
of humans and machines in today's world. These machines are
the robots have to be programmed before they start
following your instructions. But what if the machine started learning on its own from
their experience work like us and feel like us and do things more
accurately than us now? Well his machine learning Angela
comes into picture to make sure everything is working
according to the procedures and the guidelines. So in my opinion machine
learning is one of the most recent and And Technologies, there is you probably use it
at dozens of times every day without even knowing it. So before we indulge into the different roles
the salary Trends and what should be
there on the resume of a machine learning engineer while applying for a job. Let's understand who exactly a machine learning
engineering is so machine learning Engineers are
sophisticated programmers who develop machines and systems that can learn and apply knowledge without
specific Direction artificial intelligence is the goal
of a machine-learning engineer. They are computer programmers but their focus goes
beyond specifically programming machines to
perform specific tasks. They create programs that will enable
machines to take actions without being specifically
directed to perform those tasks. Now if we have a look
at the job trends of machine learning in general. So as you can see
in Seattle itself, we have 2,000 jobs in New York. We have 1100 San Francisco. We have 1100 in Bengaluru India, we have 1100 and then
we have Sunnyvale, California where we have
If I were a number of jobs, so as you can see the number of
jobs in the market is too much and probably with the emergence
of machine learning and artificial intelligence. This number is just
going to get higher now. If you have a look at the job
opening salary-wise percentage, so you can see for the $90,000
per annum bracket. We have 32.7 percentage
and that's the maximum. So be assured that if you get a job as
a machine-learning engineer, you'll probably get
around 90 thousand bucks a year. That's safe to say. Now for the hundred and
ten thousand dollars per year. We have 25% $120,000. We have 20 percent almost then we have a hundred
and thirty thousand dollars which are the senior
machine learning and Jenna's that's a 13 point
6 7% And finally, we have the most senior
machine learning engineer or we have
the data scientist here, which have the salary of a hundred and forty thousand
dollars per annum and the percentage
for that one is really low. So as you can see there is
a great opportunity for people. What trying to go
into machine learning field and get started with it? So let's have a look
at the machine learning in junior salary. So the average salary
in the u.s. Is around a hundred eleven
thousand four hundred and ninety dollars and the average salary
in India is around seven last nineteen thousand
six hundred forty six rupees. That's a very
good average salary for any particular profession. So moving forward
if we have a look at the salary of
an entry-level machine learning. You know, so the salary
ranges from $76,000 or seventy seven thousand
dollars two hundred and fifty one thousand
dollars per annum. That's a huge salary. And if you talk
about the bonus here, we have like three thousand dollars to twenty
five thousand dollars depending on the work YouTube and
the project you are working on. Let's talk about
the profit sharing now. So it's around
two thousand dollars to fifty thousand dollars. Now this again depends upon the project you are working
the company you are working for and the percentage that Give to the in general
or the developer for that particular project. Now, the total pay comes around
seventy six thousand dollars or seventy-five thousand dollars two hundred and sixty
two thousand dollars and this is just for the entry
level machine learning engineer. Just imagine if you become
an experience machine learning engineer your salary
is going to go through the roof. So now that we have understood who exactly is
a machine learning engineer the various salary Trends
the job Trends in the market and how it's rising. Let's understand. What skills it takes to become
a machine learning engine. So first of all, we have programming languages
now programming languages are big deal when it comes
to machine learning because you don't just
need to have Proficiency in one language you might
require Proficiency in Python. Java are or C++ because you might be working
in a Hadoop environment where you require Java programming to do
the mapreduce Coatings and sometimes our is very great for visualization purposes
and python has you know, Another favorite languages when comes to machine
learning now next scale that particular individual needs
is calculus and statistics. So a lot of machine learning
algorithms are mostly maths and statistics. So and a lot of static is required majorly
the matrix multiplication and all so good understanding of calculus as well as
statistic is required. Now next we have signal processing now Advanced
signal processing is something that will give you an upper Edge over other machine
learning engine is if you are Applying
for a job anywhere. Now the next kill we
have is applied maths as I mentioned earlier
many of the machine learning algorithms here are
purely mathematical formulas. So a good understanding of maths
and how the algorithm Works will take you far ahead
the next on our list. We have neural networks. No real networks are something that has been emerging quite popularly in the recent
years and due to its efficiency and the extent to which it
can walk and get the results as soon as possible. Neural networks are a must for machine learning engine
now moving forward. We have language processing. So a lot of times machine
learning Engineers have to deal with text Data the voice data
as well as video data now processing any kind
of language audio or the video is something that a machine-learning engineer
has to do on a daily basis. So one needs to be proficient
in this area also now, these are only some
of the few skills which are absolutely necessary. I would say for
any machine learning and Engineer so let's
now discuss the job description or the roles and responsibilities of a particular machine
learning engineer now depending on their level of expertise machine
learning Engineers may have to study and transform
data science prototypes. They need to design
machine Learning Systems. They also need to research and
Implement appropriate machine learning algorithms and tools as it's a very
important part of the job. They need to develop
new machine learning application according to the industry
requirements the Select the appropriate data sets and the data
representation methods because if there is a slight
deviation in the data set and the data representation that's going to
affect Model A lot. They need to run machine
learning tests and experiments. They need to perform
statistical analysis and fine-tuning using
the test results. So sometimes people ask what exactly is a difference
between a data analyst and a machine learning engineer. So so static analysis just a small part of of
machine learning Engineers job. Whereas it is a major part or it probably covers a large
part of a data analyst job rather than a machine
learning Engineers job. So machine learning
Engineers might need to train and retrain the systems
whenever necessary and they also need to extend the existing
machine learning libraries and Frameworks to
their full potential so that they could make
the model Works superbly and finally they need to keep
abreast of the developments in the field needless to say that any machine. In general or any particular
individual has to stay updated to the technologies that are coming in the market and every now and then
a new technology arises which will overthrow
the older one. So you need to be
up to date now coming to the resume part
of a machine learning engineer. So any resume of a particular
machine learning Engineers should consist like clear
career objective skills, which a particular
individual possesses the educational qualification certain certification
the past experience if you are an experienced
machine learning and Jen are the projects which you
have worked on and that's it. So let's have a look
at the various elements that are required in a machine-learning
Engineers resume. So first of all, you need to have
a clear career objective. So here you will need
not stretch it too much and keep it as
precise as possible. So next we have the skills
required and these skills can be technical as
well as non technical. So let's have a look at the various Technical and
non-technical skills out here. So starting with
the technical skills. First of all, we have programming languages
as an our Java Python and C++. But the first and the foremost requirement
is to have a good grip on any programming languages
preferably python as it is easy to learn and it's applications are wider
than any other language now, it is important to have
a good understanding of topics like data structures memory
management and classes. All the python is
a very good language it alone cannot help you so you will probably
have to learn all these he's languages
like C++ are python Java and also work on mapreduce at some point of time
the next on our list. We have calculus and linear
algebra and statistics. So you'll need to be
intimately familiar with matrices the vectors
and the matrix multiplication. So statistics is going
to come up a lot and at least make sure
you are familiar with caution distribution
means standard deviations and much more. So you also need to have a firm
understanding of probability. Stats to understand the machine
learning models the next as I mentioned earlier, it's
signal processing techniques. So feature extraction is one
of the most important parts of machine learning different types of problems
need various Solutions. So you may be able to utilize the really cool Advanced signal
processing algorithms such as wavelengths shallots curve. Let's and the ballast
so try to learn about the time-frequency analysis and
try to apply it to your problems as it gives you an upper jaw. Our other machine
learning Engineers, so just go for the next we have mathematics and a lot of
machine learning techniques out. There are just fancy types
of function approximation having a firm understanding
of algorithm Theory and knowing how the algorithm works
is really necessary and understanding subjects like gradient descent
convex optimization quadratic programming and partial differentiation will
help a lot the neural networks as I was talking earlier. So we need machine learning
for tasks that are too Flex for humans to quote directly. So that is the tasks
that are so complex that it is Impractical neural
networks are a class of models within the general
machine learning literature. They are specific
set of algorithms that have revolutionized machine learning deep neural networks
have proven to work quite well and neural networks are themself General
function approximations, which is why they can be applied to almost any machine
learning problem out there and they help a lot
about learning a complex mapping from the input to The output space now next
we have language processing since natural language
processing combines two of the major areas of work that are linguistic
and computer science and chances are at some point
you are going to work with either text
or audio or the video. So it's necessary to have
a control over libraries like gents mm and ltk and techniques like
what to wet sentimental analysis and text summarization Now voice and audio analysis involves
extracting useful information from the Your signals themselves
very well versed in maths and concept like Fourier
transformation will get you far in this one. These were the technical skills
that are required but be assured that there are a lot
of non technical skills. Also that are required
to land a good job in a machine learning industry. So first of all, you need to have
an industry knowledge. So the most successful
machine learning projects out. There are going to be those
that address real pain points, don't you agree? So whichever industry
are working for You should know how that industry works and what will be beneficial
for the industry. Now, if a machine
learning engineer does not have business Acumen
and the know-how of the elements that make up
a successful business model. All those technical
skills cannot be channeled productively. You won't be able
to discern the problems and the potential challenges
that need solving for the business to sustain
and grow the next on our list. We have effective communication
and not this is one of the most important parts
in any job requirements. So you'll need to In machine
learning Concepts to people with little to no expertise
in the field a chances are you will need to work
with a team of Engineers as well as many other
teams like marketing and the sales team. So communication is going
to make all of this much easier companies searching for the strong machine learning
engineer looking for someone who can clearly and fluency
translate technical findings to a non technical team. Rapid prototyping
is another skill, which is very much required for
any machine learning engineer. So iterating on ideas as
quickly as possible is mandatory for finding the one that works in machine learning
this applies to everything from picking the right model to working on projects
such as a/b testing and much more now you
need to do a group of techniques used to quickly
fabricate a scale model of a physical part or assembly using the three-dimensional
computer aided design, which is the cat data now coming
to the final skills, which will be required for any machine learning agenda
is to keep updated. So you must stay up to date
with any upcoming changes every month new neural
network models come out that outperformed
the previous architecture. It also means being aware of the
news regarding the development of the tools Theory and algorithms through research
papers blocks conference videos and much more. Now another part of any machine
learning engineer's resume is the education qualification. So a bachelor's or master's degree in computer
science RIT economics statistics or even mathematics can help. Up you land a job
in machine learning plus if you are an experienced
machine learning engineer, so probably some standard
company certifications will help you a lot when Landing a good job
in machine learning and finally coming
to the professional experience. You need to have experience in
computer science statistics data as is if you are switching from any other profession into
a machine learning engineer, or if you have a previous
experience in machine learning that is very well. Now finally if we talk
about The projects so you need to have
not just any project that you have worked on you
need to have working on machine learning related projects that involve a
certain level of AI and working on neural networks
to a certain degree to land a good job as
a machine-learning engineer. Now if you have a look
at the company's hiring machine learning Engineers, so every other
company is looking for machine learning Engineers who can modify the existing
model to something that did not need much more. Of Maintenance and cancel
sustain so basically working on artificial intelligence
and new algorithms that can work on their own is
what every company deserves. So Amazon Facebook. We have Tech giants like Microsoft IBM again
in the gaming industry, we have or the GPU
industry Graphics industry. We have Nvidia
in banking industry. We have JPMorgan Chase again, we have LinkedIn
and also we have Walmart. So all of these companies
require machine learning engine at some part of the time. So be assured that if you are looking for a machine
learning engineer post, every other companies be it
a big shot company or even the new startups are looking
for machine learning Engineers. So be assured you will get
a job now with this we come to an end of this video. So I hope you've got
a good understanding of who exactly are
machine learning engineer is the way just job Trends
the salary Trends. What are the skills required to
become machine learning engineer and once you become
a machine-learning engineer, what are the roles
and responsibilities or the Job description what appears to be on the resume
or the job description what appears to be on the job application of
any machine learning engineers? And also I hope you got to know
how to prepare your resume or how to prepare it
in the correct format. And what on to keep their in the resume the career
objectives the skills Technical and non-technical previous experience
education qualification and certain projects which are related to it. So that's it guys Ed Rica as you know provides
a machine learning. Engineer master's program now
that is aligned in such a way that will get you acquainted
in all the skills that are required
to become a machine learning engine and that too
in the correct form.
