How I'd Learn AI in 2024 (If I Could Start Over) | Machine Learning Roadmap

here is my prediction AI is going to be the biggest trend of 2024 and going forward in this decade hi everyone I'm asan Sharma I started learning about machine learning and AI back in 2019 and 2020 and today this field is booming like never before with the launch of chat GPT and other generative AI applications there is so much demand for AI engineers and because of that if you want to become one this video is the one you need to watch we even launched centur which was a generative AI app last year I'll share with you everything you need to learn to become an AI engineer from zero to building generative AI applications in the next 20 minutes we'll break it into steps talking about the exact free courses that you can take and become an AI engineer for completely free and if that sounds exciting hit the like button and subscribe and let's start with the topic of today's video how I would learn Ai and machine learning in 2024 if I had to start all over again the most important thing to understand is what is AI or machine learning the basic definition is machine learning is a process through which a system can recognize patterns and predict future outcomes that is the basic idea of it so imagine you have a blackbox you first give it an input and you tell what the outut come is and hence you are training this blackbox what happens next is it tries to identify patterns between the input and the output and once you've given it enough training data you then ask it a question and then you ask it to give you an outcome and a probability for that outcome and that is what machine learning is all about again you don't need to stress about anything we'll break everything down into steps in the next few minutes the first first important thing to learn is going to be maths maths will be the foundation upon which you'll build all of your learnings so the most important thing to learn in maths will be calculus differentiation and integration understanding about linear algebra and lastly probability you need to have an intuitive idea of what these things really mean first of all you should understand integral calculus and differentials you should understand how matrices really work and what is the dot product of two matrices and at the end you should understand understand about probability probability is going to be very important as we move on because any result that a model gives is not a yes or no it is a probability it will say that I think this photo looks like a dog the probability of that is .95 I think this photo does not look like a dog the probability for that is98 all of this is very simple you can you can learn it from three blue one Browns videos on calculus as well as matrices and probability or you can go to KH Academy or free code Camp has some amazing videos on mathematics so go and have a look at those it's very simple and once you understand the basics of maths now we can move on to the next step right now all of the things that we are learning in maths we'll be applying when we'll be building these machine learning models when we will be training these models when we will get an outcome from this model so it's all going to be relevant stick around till the end the second step is to learn about python now python is the most used programming language I think in this age after JavaScript maybe and it's super easy to learn it's similar to English I learned it myself in 2019 I even taught python to thousands of students back in 2020 and 21 on an academy so it's a very simple language to learn right I I taught it to school kids if you're in college you can very simply learn it now Tech with Tim has a great playlist which will give you the basic idea of python or you can also check out free code cams tutorials on python but you need to understand the basics of this programming language because this is what you will use to build all the models the first thing to learn is data types learn about F's conditional statements learn about for Loops how can you create functions and then learn about oop Concepts and when you have have all of these things figured out try building some simple projects try just rebuilding some games and see how well can you do it and you can even take a step further and learn about the different libraries that python really has and that will give you a basic idea of how do you use your knowledge of python Concepts and apply it into building something in real world right and that is super important so once you have the basic idea of python again you don't need to know every single thing because I think learning in 2024 is a lot less about completing the entire textbook knowing all the rules of the game it's more about understanding the basics going about with it if you face any difficulty error or something that you don't know Google it stack exchange you can also just ask chat GPT for some answers but most important is to learn the basics and then keep moving forward because what will happen is you'll find it too hard to learn everything about python python has just too many libraries too many packages too many applications don't focus on all of that just understand the basics and keep your eye on the goal which is to become an AI engineer the next step after learning python is to learn about data analysis and data analysis with python is all about using three libraries the first one is numai second one is pandas and the third one is is matte plot Li numi will basically help you with numbers you will be storing all the data in arrays and then you'll have multi-dimensional arrays which will become matrices and then you'll be doing a lot of dot product of matrices to come up with answers again this will be relevant when you'll be building the models when you'll be coming up with parameters when you'll be optimizing an equation I'll talk all about that in later but you will have to use numpy arrays to do all of that the second thing is pandas pandas is a library which will basic basically help you with data which is in tabular form right so any data that you have stored in columns and rows pandas will help you get it sorted and this is super important because any data set that you use or you make it it's all going to be in a file called CSV comma separated values and you should know how to use it and how to extract data from that table from that tabular form and you can do that very easily with pandas again it all has functions that you can use so it'll be like PD do this or the same is the case with numpy arrays so you create an array and then you say np. array and then you perform functions on those arrays right that is how it works the last library that I talked about in data analysis is going to be matte plot lib matte plot lib is really important because once you run the model you have some outcomes you have some answers you have a probability and you have to Showcase it you need to show what the end result is and that is what you do with the help of matplot lib it's a amazing Library through which you can actually plot bar charts you can plot pie charts you can plot various ways to represent data right and that is where Matt plot lip comes very handy you use numpy you use pandas and you use Matt plot lip to analyze any data set that you would be getting so once we've learned about python once we learned about data analysis the next step is to pick pick a framework now there are various Frameworks which make your life easy you can use these Frameworks and you can very easily create different types of machine learning models you can either choose py torch pyit learn or tensorflow or there are many others as well but these are the three most popular ones I would suggest you to go with pytorch or psyit LA because these are simpler Frameworks to use as a beginner tensor flow just hides a lot of the beginner stuff away from you so it might look really clean you can just use three four lines of code to execute something but you won't understand the essence of what you are building hence tensorflow you can pick once you have the basic idea of building models and tensorflow makes it really simple and you can quickly spin up models with the help of tensorflow and kasas now the most important thing here is to pick one framework and master it you can find numerous tutorials on free code Camp about p TCH along with psychic learn so you focus on learning each of these what will basically happen is you learn about the various types of machine learning models essentially there are three types of machine learning models the first one is a supervised learning the second one is unsupervised learning and the last one is reinforcement learning in supervised learning the data that we give to the model is labeled data so we are actually telling it that this photo is of a cat and this photo is of a dog and when you have un learning in that case data is unlabeled right there's no label on the data the model can just tell that this is category a and this is category B that is what unsupervised learning is all about and the last thing is reinforcement learning and reinforcement learning is all about creating an incentive for a character in a game or any environment and then it learns how to optimize for that number for example if you have a character in a game you create an incentive that when it walks towards the price it gains more points when it walks away from it it will lose points and hence you are basically training the model telling it that you should move closer to the price to increase your points and the goal of this game is to increase the points as much as possible so that is your reinforcement learning and these are the three main types of machine learning models that you can create now there's an amazing course on course era that you can take by Deep learning AI called as a machine learning specialization you can individually enroll and audit each of the three courses that are in that specialization for completely free and you can learn about all of these Basics right so when you go through that it is I think the most popular course in machine learning so take that course and you learn the basics of supervised learning un supervised learning along with reinforcement learning now in supervised learning you have various types you can either have regression problems or you can have a classification problem right regression is the one in which you have some data and you have to predict future data and in the case of classification as the name suggests you have to classify between two or three or more categories so you can create multiple types of models regression analysis can have logistic regression then you have polinomial regression then you have classification models along with that you also have something called as K nearest Neighbors and all of these are going to help you predict something it is all going to help you train that model these are all approaches to solve a particular problem you need to choose which one is the best and when you go through different practices when you try things out a lot you will understand which model to use for which type of problem that is essentially how it works in UNS supervised learning you have clustering which is called as K means clustering and then in reinforce po learning there's just one type which I've already explained to you in which you are optimizing for the number of points of that character now for reinforcement learning particularly Tech with Tim on YouTube has a tutorial on Flappy Bird AI so go check that out and you will learn exactly how to build it from scratch and you will understand how can you incentivize a character which is the Flappy Bird to actually play the game on its own with the help of reinforcement learning so go check that out but basically once you learn the different types then you can start going deeper and building projects The Next Step would be to practice as much as possible with the knowledge that you now have accomplished so you go to kaggle and you start solving problems they will be data sets you take them and then you try to solve the problem that they have stated there are very famous data sets like the Titanic data set the most cliched one you have cfr1 you have Mist you have fash mnist and you can start applying all of your learnings on these data sets Titanic data set would have information about the passengers on board Titanic and you have to predict something the second one cfr1 has random images of 10 different objects and animals now mnist is a collection of images which will be having numbers written in it and you have to basically build a model that recognizes those digits and lastly we have fash mnist which is going to be clothes so it should be able to identify is this a jacket is this a lower is this a cap is this a shoe things like that I hope you're understanding what I'm saying now when you're are solving for amnest or fash amnest you need to learn about something called as neural networks and that is where we start our journey into deep learning right neural networks is a very interesting and fascinating concept just think of it like a layer of neurons in which you'll be having inputs and outputs you are having multiple inputs you're putting that through a black box which comprised of a layer of neurons and then you have an output you are training this model to actually optimize and get the best result possible and here is where you should learn about the concept of back propagation right the next step is for you to have multiple layers of neural networks and that is deep neural networks right so you would be having multiple layers you would be having an input layer an output layer and multiple layers in the middle depending on how complex the problem is so the more random the situation is the more layers you will need to effectively solve a problem and that is essentially how it works you will also then have to learn about something called as hyper parameters you learn about keywords like learning rate weights and biases and how do you manipulate these three numbers to get the optimal output to get the best probability for the right output so that is how it works again to learn about neural networks you can check out this amazing tutorial by Andre kapati on YouTube it's completely free you can also check out this amazing cs50 AI course wherein they take you in the depths talking about machine learning and the neural networks so check out that part where they talk about neural networks that will open up your eyes a lot so once you know about neural networks The Next Step then is to move to the next thing which is called a CNN convolutional neural networks which will again be discussed in the cs50 artificial intelligence course so you will learn how do you use images and you classify between images see an image is built up of pixels and each pixel has a RGB value red green and blue so this will be having three values on one pixel and there can be let's just say 108 T into 1920 now think of these pictures in a data set which is labeled so it says that this is a dog this is a cat and you're feeding it into a neural network and hence you would basically create a neural network which looks sort of like this and this is the idea of CNN convolutional neural networks again what happens in between is the blackbox and your model will try to understand the patterns and will optimize those weights and biases and learning rate and EPO and everything to come up with the accurate outcome and the more you train it the better the outcome will become so that is essentially how that works you will learn about CNN the next step is up to you you can learn about NLP natural language processing you can also learn about rnns and there's an amazing course by hugging face on NLP that you can check out and get started with that so now that you've learned the basics of deep learning deep neural networks convolutional Neal networks the next step is for you to start tinkering around with ch GPT and generative AI you can now perfectly start building AI tools because you have the basic underlying knowledge of how these neural networks really work the next step is to check out deep learning AIS tutorials on chat GPT the first one is about how chat GPT works you'll understand how large language models operate and you'll also learn about the art of prompt engineering ing that will get you started they have other courses as well which talk in depth about using the open AI apis to build your own models from scratch it also talks about how do you build a large language model from scratch how do you customize a large language model how do you use tools like Lang chain to actually use models in any particular environment so there's lots of things that you can explore now check out deeplearning.ai they have some amazing short courses that you can take to learn the basics of large language models you can learn about how do you deploy these models how do you take advantage of the existing tools like hugging face and chat GPT and stable diffusion and mid journey and others to build your own tool on top of it you can also explore the chat GPT documentation to learn more about how does it really work and what you can do with it you can do the same with other tools like hugging face like Lang chain and others and that will give you a perspective on how do you build with these tools from scratch the next step is for you to learn about Chad GPT plugins and the GPT store now this is a amazing opportunity it's like the App Store from the 2008 in which you would basically able to build and train your own custom GPT you can launch it and people would pay you to get access to those gpts again you will have to customize these gpts you will have to learn the art of prompt engineering and that is how you can build on top of chat GPT on the GPT store so that is what you need to learn if you want to become an artificial intelligence engineer in 2024 there's a lot more to learn you are just getting started description will have a ton of resources that you can take and get started in your journey of becoming an AI engineer if you have any questions let me know Below in the comment section but this is how you do it in 2024 learn about python then python libraries then learn about machine learning then learn about neural networks and then finally learn about NLP and then go into llms and start building your own generative AI applications thank you so much for watching any questions let me know Below in the comments if you're still watching in the comment section I watch till the very end you can also click a screenshot and post this on social media and tag me at isan Sharma 7390 I will catch you in the next video byebye [Music]

Channel: Ishan Sharma

Views: 375,320

Keywords: ishan sharma, ai, ml, artificial intelligence, machine learning, ai engineering, ai career, ai ml jobs, machine learning jobs, machine learning career, how to become ai ml engineer, how to become ai engineer, developer, development, ai developer, ml developer, how to be an ai dev, how to become an ai engineer, ai developer roadmap, ai engineer roadmap, ai developer course, ai developer guide, ai for beginners, how to learn ai, free courses, ai courses, ml courses

Id: H-RKfV2ZJbI

Length: 20min 17sec (1217 seconds)

Published: Sat Mar 02 2024