hey my name is Felipe and welcome to my
channel, in this video I'm going to show you a fully comprehensive computer vision roadmap
I'm going to show you all the skills you should learn to become a computer vision engineer and all the different ways
in which you can specialize in computer vision and I'm also giving you very specific resources
you can use in order to learn all the skills I show you in this roadmap and now let's get started so let's get started with
this computer vision roadmap, in this video I'm going to show you an entire roadmap in
order to go from zero from scratch from having absolutely no background in IT whatsoever up to a
complete expert computer visual engineer so let's get started the first step you should follow
in this roadmap is covering the fundamentals and when I say fundamentals I mean Python and
opencv these two skills are definitely the most important skills you should start with in order to
become a computer vision engineer, and this video, this computer vision roadmap is an updated version
of one of my previous videos, and in this version I have added some specific resources you can take
you can follow in order to learn all the different skills I am going to show you in this roadmap so
in order to Learn Python and in order to learn OpenCV you can take a look at these two resources I have
added over here and regarding opencv this is a three hours long fully comprehensive course of
opencv with python and I definitely recommend you to check out this course but if you do not
have three hours in order to take this course then these are the most important lessons you
should take in order to cover the basics of opencv with python so if you do have three hours
then please take a... take a look at this course but if you do not don't worry you can just take some
of these lessons and you are going to cover the most important aspects of opencv with python so
this is the first step you should take in order to learn computer vision, in order to become a computer
vision engineer and now let's continue the next step in this roadmap is the basics of machine
learning, machine learning is very very very important in computer vision and these are the
most important things you should learn, these are the four most important tasks in computer vision
image classification object detection semantic segmentation and pose detection and this is the
way I recommend you to learn machine learning by learning how to solve these four very specific
problems, these four very specific tasks, so by learning how to build an image classifier how to
build an object detector how to build a semantic segmentation algorithm and how to build a pose
detector oh my God you will have learned so much machine learning and you will be super super
proficient in machine learning these are the most important machine learning tasks in computer
vision and these are some very specific resources you could use in order to learn how to solve
these problems right these are some courses and some tutorials I recommend you in order to
solve these problems in order to learn how to solve these problems and by doing so by solving
these problems you are going to learn how to use some very specific tools which are... let me show you
scikit learn yolo V8 yolo nas pytorch tensorflow and many many many more tools and please pay
attention because this is very very very important these are the tools we use in order to solve these
problems right but the important thing is not the tools we use the important thing is the problems
we are solving by using the tools right so my recommendation for you is do not focus on learning
how to use the very specific tools but focus on how you are going to solve each one of these
problems and by doing so you are going to learn how to use different tools right my recommendation
for you is do not focus on learning the tools focus on learning how to solve these very specific
problems but if you are one of those people who prefer to learn the tools that's perfectly fine
I have also added some resources for you right because I know we all have different preferences
so if you prefer to learn how to use the tools these are some resources you could use right
but remember my recommendation is do not focus on the tools focus on how you're going to solve
the high level problems image classification object detection semantic segmentation and pose
detection which are perhaps the most important machine learning problems in computer vision now
let's continue and you can see that from here from basic machine learning you can take it in either
one of these two paths let's take it over here for now and later on I'm going to show you what's
over here so following this path we have the specialization right now that you feel confident
in Python and opencv, now that you have learned the basics of machine learning now it's time to
specialize right and you have many different ways to specialize one of these different ways is low
level programming and electronics which basically involves C++ and how to work with an
edge device for example Arduino or jetson nano and the reason this is one of the ways in which
you can specialize in computer vision is because although C++ is a very very very important
programming language you can definitely take up many projects as a computer vision engineer
and you can just become a computer vision engineer without really doing anything related
to low level programming or anything related to electronics right it's perfectly possible so this
is one of the ways in which you can specialize if you want to go deep into low level programming if
you want to go deep into working with electronics working with robotics for example then you can
just specialize and you can do it learning C++ and learning how to work with this type of
edge devices now another way in which you can specialize is by taking the research path
right, by doing research and this involves learning very advanced machine learning
and also very advanced mathematics there's a huge misconception in computer vision which is that you
definitely need very advanced mathematics and you differently need to know how to work with very
Advanced mathematical objects and operations in order to do computer vision and that is not true
that's a misconception that is false I can tell you that you can definitely work in the field as
a computer vision engineer and you can definitely take many many projects and you can definitely
make a lot of money as a computer visual engineer by knowing Python opencv and the basics of machine
learning only by knowing these skills you will be able to solve the machine learning part of many
many projects and you will be able to solve many projects. Knowing the advanced mathematics and
the advanced machine learning and the advanced everything is not absolutely needed that's why this
is... I consider this is one of the ways in which you can specialize right and if you want to take this
route these are some very specific resources you could take right and I forgot to mention these
are some very specific resources you can follow in order to learn this other way to specialize in
computer vision which is low level programming and electronics but let's continue now let's take
it to the other way which you can specialize which is generative Ai and this basically involves
image generation and also text generation right this is something that I would say it's huge already
it's already a very very important field in computer vision and my thoughts are that in the
next few years this is going to be bigger and bigger this is going to be a very very important
field in computer vision and these are some very specific tutorials from my own YouTube channel
you can take in order to learn how to work with image generation and text generation in a computer
vision project now let's continue these are some of the ways in which you can specialize
in computer vision these are definitely not all the ways in which you can specialize there
could be other ways but I think these are some of the most important paths you could take as
a computer vision engineer but now let's take it back remember that from basic machine
learning we could take another path right let's see what's over here and this is where we have
all the software related skills right because remember as a computer vision engineer you are
not working on a vacuum right you are working with other software developers you are building
products you are doing some things which involve software so the more you know about software
the more software related skills you may have is going to be much much more better for you
and these are some very specific examples of some very specific software skills which are very
important in computer vision as a computer vision engineer you definitely need to know how to work
with a Version Control software for example GitHub you'll definitely need to know how to work with
Docker is going to be a plus for sure in your career if you know how to work with Docker it's
very important you know how to work with a cloud provider with a cloud development platform for
example AWS Google cloud or Azure and it's also a plus it's something very important if you are
familiar with web development technologies let me tell you a very quick story about me about myself
when I was just starting in computer vision I completely underestimated how important it is all
these software related skills and I thought that by learning Python opencv and the Very basics
of machine learning the very Basics by learning how to build an image classifier, the very basics
of machine learning I was all set I was ready to work in the top companies in the field to work
in Google in Tesla in SpaceX... I thought I was ready, that was all, that was it, and I was
wrong I was so so super wrong for many different reasons and one of them is because I completely
underestimated all these software related skills if you are going to work in computer vision
you'll definitely need to know something other than computer vision and this is very important
because this is very often underestimated by many people in the computer vision industry or in
the machine learning industry there are many people who believe who think that by learning
the basics of machine learning that's it that's not all if you're going to work in the
field or you need to know something other than computer vision and these are some very specific
resources you could take in order to learn all these skills and also remember that working in a
company as an employee it's only one of the many many different career choices you can take in your
professional career you could also be something like a freelancer, a freelance computer vision
engineer, and if you decide to be a freelancer and a client hires you to do something like
building a machine learning model, building an object detector and serving this model through
an API and you tell the client 'okay I can help you building the model but in order to serve it
through the API hire someone else because I don't even know what's an API' the client is going to
say something like 'oh okay okay I am going to hire someone else, I'm going to hire someone else
to do the entire project is going to be much more affordable it's going to be much more easier to
manage than if I hire many different developers to make each one of the tasks in this project' so
this is especially the case if you decide to be something like a freelancer because the moment
you tell your client you don't know how to do something the moment this client replaces you by
someone else so this is very important if you want to be a freelancer as well now let's continue
I have already show you the entire roadmap you should take in order to become a computer vision
engineer with very specific resources and I have also shown you all the different ways in which
you can specialize as a computer visual engineer now let's continue because now it's time to
show you how to enhance your skillset, how to grow your skills and one of the ways in which you
can grow your skills as a computer vision engineer is by working on projects by making projects by
doing projects by hands-on experience working on projects and there are two different ways in
which you can do that one of them is by following coding tutorials and projects in YouTube,
and there are many many projects you can take you can do on YouTube these are only a few examples
of some of my tutorials of my projects in this YouTube channel and you can also take many paid
courses right if you want to take your skills a step further if you really want to become like
an absolute expert then you also have many paid resources you could use in order to enhance your
skillset even further right and these are only a few examples also from my own paid products for
example this is a project which is available in my Patreon and in this project I show you the entire
process of how to build a video summarization API I take you from the requirements up to the project
deliverable and I show you every single step of this process how to do the planning how to do the
system design how to work in the execution every single step of this process and this is exactly
how a real world computer vision project looks like, then this is another example this is also
available my Patreon and this is a very Advanced lesson on how to train a machine learning model and it
involves how to control the randomness when you are training a machine learning model this is
a very very Advanced lesson and these are a few resources you will take in order to enhance your
skillset as a computer vision engineer and then you also have other resources and in these other
resources are for example books you could read books on computer vision in order to improve
your skill set as a computer vision engineer these are only a few examples of some of the books
you could read in order to become a super absolute expert computer vision engineer then another very
interesting resource is joining a community and these are some examples this Discord server is
this YouTube channel Discord server right this is our community and this is a very very interesting
resource the way usually work is that the members of our community post the projects in which
they are currently working in and everyone else recommend this user different things he or she can
do with his or her project we say something like hey have you tried to do this have you tried to
do the other thing have you tried to do this with the data I don't know we collaborate in many
different ways so we help everyone with their computer vision projects this is a very very very
interesting resource in order to go deeper into your knowledge of computer vision then these
are some subreddits you could consider, computer vision, machine learning, these are some super
high levels subreddits but you also have many other subreddits which are very very valuable
in order to learn more about a very very specific niche for example this one about stable diffusion
I have used this subreddit a lot lately because I have been learning a lot about stable diffusion and
this subreddit is perhaps one of the most valuable resources in order to learn stable diffusion
so these are only a few examples of some of the communities and some of the subreddits you could
use and then another very interesting resource in order to go deeper into your computer vision
knowledge is joining a competition, competing with other people that's oh my God that's going
to take your skill set a step further for sure in order to do so I recommend you to use kaggle
which is perhaps the most important and the most relevant site in order to do competitive
computer vision in order to join a competition in which you have to train a computer vision
model and compete against other competitors so this is going to be all for this computer vision
roadmap please let me know what you think in the comments below if you enjoyed this video I invite
you to click the like button and I also invite you to subscribe to my channel this is going to be
all for this video and see you on my next video
