hey my name is Felipe and welcome to my channel, in this
video I'm going to show you a machine learning and AI roadmap, I'm going to show you what are all
the skills you should learn in order to become a machine learning and AI engineer and I'm also
going to give you very specific resources you can use in order to acquire these skills, all of
the resources I'm going to give you in this video are publicly available so you can just use them
for free and this is exactly what we will be doing today, and it's very important: this roadmap
is made for beginners, for absolute beginners, so even if you have absolutely no background in
it whatsoever no background in programming in machine learning and AI, don't worry this roadmap
is for you and now let's get started. And now let's get started with this machine learning
and AI roadmap and the first step in this process will be covering the fundamentals remember
machine learning and AI are some very complex fields so it's very important to build some very
strong foundations in order to continue, and this is where you are going to Learn Python which is
one of the most important programming languages and also scikit learn which is a python Library
very commonly used to work with machine learning and these are some very specific resources
you can use in order to learn Python and scikit learn, so these are the skills you're going
to learn in the fundamentals which is the first step in this process, and regarding scikit learn
there's another resource you can use which is the scikit learn official documentation let me
show you this documentation over here this is another very important resource to get more
familiar with scikit learn and this is... these are some links to some very specific parts in
this documentation where is where you're going to learn how to preprocess data. how to work
with dimensionality reduction which is a very important technique in machine learning, how to do
classification, clustering and how to work with the most important metrics in machine learning so
this is the first step in this process which is the fundamentals. Please do not underestimate
this step because remember everything that comes from now on it's built on top of the foundations,
of the fundamentals, so it's very important you properly understand everything that's here Python
and scikit learn, now let's continue and the next step in this process will be problem solving, we'll
be getting more familiar with the most important problems in machine learning, these are only a few
of the most important problems in machine learning, which are object detection, image classification,
text classification and time series forecasting but remember these are only a few problems to
get started these are not by any means all the problems you will encounter in machine learning
these are only a few of them right, these are only a few of the most important problems in machine
learning, let me show you something, I asked chatgpt to give me a very comprehensive list of all the
high level problems in machine learning and this is what chatgpt gave me right, you can see
that this is a very very comprehensive and a very long list of many many problems, these are
all the problems we are going to encounter in machine learning and you can see that this is a lot
and if we go back to our roadmap you can see that I have selected only four problems so this is only...
these are only a few problems to get started right this is only to get started you can see that we
have object detection, image classification, text classification and time series forecasting and
if we go back to this list over here you can see that object detection is over here, then image
classification is over here, text classification is over here, and then time series forecasting is over
here, so these are only four of all the problems we have over here this is only like a very very very
very small selection of all the problems we have in machine learning so this is the second step in
this process, to get more familiar with the high level problems you will encounter while working in
machine learning or at the very least to get more familiar with these... only with these four problems
which are some of the most important problems right, object detection, image classification, text
classification and time series forecasting, these are some very specific resources, these are some
courses and some tutorials you can use in order to acquire these skills in order to know how to
work with these problems and then along the way while you're are working in this process, while you
are learning how to solve these problems, you are going to use many tools right, you are going to use
opencv, yolov8, pytorch, tensorflow, fbprophet, highing face and many many many more tools right, so while you are working
towards the solution of all these problems you are going to use tools right and these are only a
few of the tools you are going to use but this is very important and this is something that I have
mentioned in my previous tutorials, remember when you are trying to solve a problem you are going
to use many tools right obviously you are going to use many tools but the most important thing...
the most important thing you should focus is in the problem you are trying to solve, not in the
tools you are using to solve the problem right, this is about problem solving so you should focus
on how to solve each one of the problems you have over here, you should focus on understanding
exactly what object detection is and what are all the techniques to do object detection, what is
image classification and what are all the techniques to do image classification and so on, the same goes
for text classification, time series forecasting and so on, the most important part in this part of this
process is properly understand these high level machine learning problems and how to approach
them, how to solve them, right, during the process of solving these problems you are going to use
many tools yeah obviously but the most important thing is not the tools you use the most important
thing is the problems you solve by using the tools, this is very important and I cannot stress
this enough, the second step in this process is about problem solving and this is where you should
focus on properly understand all these high level problems in machine learning and how to approach
them, remember these are only a few problems to get started but at the very least you should be
familiar with at least these problems in order to know how to solve machine learning problems
and in order to continue, now let's move to the next step in this process which will be software
related skills, this is something that I have also mentioned in my previous videos, remember machine
learning and AI doesn't work on a vacuum right if you're going to do machine learning then you
need to know something other than machine learning you definitely need to know some software related
skills and these are some of the software related skills you should be familiar with, for example
you should be familiar with Docker you should be familiar with what is Docker and you should have
like a high level understanding of how to use Docker in order to solve problems, also the same
goes for cloud to working in the cloud working with a cloud provider for example AWS google cloud
or azure, it's very important you have like a high level understanding of how to work in the cloud,
the same goes for web development you don't have to be an expert in any of these technologies but
you do need to be familiar with how to work with them right this is very important machine learning
doesn't work on a vacuum so you definitely need to know how to work with software... if
you want to work in machine learning, the same goes for databases it's very important to know what
is a database and how to work with a database and what are the different type of databases
and so on and GitHub, GitHub is perhaps one of the most important software related skills you
need in order to work in machine learning, forget about not knowing GitHub if you're going to work
in machine learning, it's very important, so these are only a few of the most important software related
skills you should be familiar with if you're going to do machine learning and also remember
that working in a company as an employee is only one of the many career choices you can make
in your professional career, you can also be something like a freelancer or something like an
entrepreneur, like an indie hacker, right, building products, and if you decide to be a freelancer or
an entrepreneur this especially applies to you, you definitely need to be familiar with different
software related skills in order to be proficient, in order to be successful, as a freelancer or as
an entrepreneur, machine learning only by itself is not going to do it you definitely need to know
some software related skills as well, so these are the software related skills you should be familiar with if
you want to work in machine learning and now that you have built some very strong foundations and
now that you feel familiar and you feel confident working with many different machine learning
problems now is the time to specialize right remember AI is a very very huge field so
it's very important to specialize and these are some of the ways in which you can specialize, for
example you can take this route which is computer vision and this is where you are going to become
a computer vision engineer, this is a very popular specialization in machine learning, in AI, and this
is actually the one I have chosen right, I am a computer vision engineer, so if you decide to be a
computer vision engineer as well this is exactly a computer vision roadmap you can follow in order to
become a computer vision engineer, now another way in which you can specialize is by learning
natural language processing and this is also a very popular field in AI and this is very popular
especially nowadays with everything that's going on with chatgpt and with large language models, this is
definitely a very interesting specialization in AI, natural language processing is very popular
and my thoughts are that it's going to be more and more popular in the future so if you decide
to specialize as a natural language processing engineer it's a very interesting choice, and this
is a roadmap you can follow in order to become a natural language processing engineer, and then
another way in which you can specialize in AI is by learning data science is by becoming a
data scientist, this is a very popular field in AI and this is where you're are going to work
with data you're going to process data you're going to analyze data you are going to visualize
data which is also a very interesting and a very important task, so this is also a very popular
field in AI then another very very popular field is robotics, you could become a robotics engineer,
a robotics expert, you have seen there have been some huge advancements in robotics in the last
few years and my thoughts are that this is just going to be bigger and bigger in the years to
come so this is definitely a very interesting application of AI, this is a very interesting
field of AI, and this is a roadmap you can follow in order to become a robotics engineer, now
moving on this is another field in which you can specialize in AI which is audio processing right,
you could become an audio processing engineer and this is very important and a very interesting
application because so far everything that I have mentioned so far, computer vision, natural
language processing, data science, robotics, these are all very very popular fields but there are
other fields which are very interesting but they may not be as popular and audio processing is
one of them, this is very interesting there are some very very interesting applications for
example everything that's related to speech recognition is here in audio processing, so this
is definitely another very interesting field to consider and this is why I have mentioned this
other field over here and this is a roadmap you can follow in order to become an audio processing
engineer, it may not be be as popular as everything else but it's very interesting, definitely it's
a very very interesting application, now let's take it back... these are all the different ways
in which you can specialize in AI, but if we go back here, remember that from problem solving
we went to software skills but there is another route you can take in order to acquire more
skills which is methematics right these are all the mathematics courses you can take in order to
acquire some very important skills which are mathematics related right, this is very important
in machine learning and AI but please mind that it says optional, so mathematics is very important
for sure in machine learning, in AI, in engineering as a whole, mathematics is very important but I
would say that if you're just starting in machine learning and AI I would recommend you to focus
on everything else, I would recommend you to focus on the fundamentals, I recommend you to focus on
problem solving, I definitely recommend you to become very very fluent, very familiar on how to
solve machine learning problems and then at the end of everything else, once you have completed
all this roadmap, then my recommendation for you is that only then you take a look at all
these mathematics related skills and in the meanwhile just focus on learning everything else,
the fundamentals, problem solving software related skills and so on and to work on many many projects
and remember these are only a few of the machine learning problems you need to be familiar with
in order to get started so once you are familiar with these problems then just continue with other
problems from here right this is by far way more important, to get familiar machine learning and AI,
way more important than mathematics, or that's my point of view so this is another... these are other
skills you could be familiar with if you decide to be a machine learning and AI engineer but remember
my thoughts, just focus on everything else first and then learn mathematics so this is going
to be pretty much all for this machine learning and AI roadmap but I have also prepared a lot
of projects in which you can work in in order to acquire the skills you are going to learn while
you are working in this roadmap, you can see that these are many many many projects, all of these
projects are from YouTube, some of these projects are from my own YouTube channel and all the other
projects are from other amazing creators also from YouTube, so all of these projects are publicly
available, they are all 100% free and these are many projects you can use in order to acquire all
the skills I mentioned in the roadmap, you can see I have divided all of these projects by all of
the ways I mentioned in which you can specialize in AI which are audio processing, robotics, data
science, natural language processing, computer vision, so you can just work on the projects you
want, depending on the way in which you... in which you want to specialize in
AI right for example if you want to be a computer vision engineer like me then you are going to
focus on these projects over here, if you want to work with natural language processing then
you're going to focus on these projects, if you want to be more familiar with audio processing,
if you want to know what is this about, then you are going to do these projects over here and
so on, just focus on the projects you like the most, just work on all of them, just do whatever
you want, so this is going to be all for this machine learning and AI roadmap, let me know in
the comments below what you think about this video and let me know what's the field in which you
are going to specialize in AI, this is going to be all for today, my name is felipe, I'm a computer
vision engineer and see you on the next video.
