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 vision 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 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 post 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 ckit learn yellow 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 you 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 post 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 in opencv now that you have learned the basics So 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 layer 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 definitely 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 vision 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 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 in which you can specialize which is generative ai and this basically involves imagination 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 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 definitely need to know how to work with docker it's 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 was i thought I was ready that was all that was it and 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 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 you believe who think that by learning the basics of machine learning that's it that's not all if you are going to work in the field or you need to know something more 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 a object detector and serving this mode through an api and you tell the client Okay, I can help you building the model, but in order to service 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 showed you the entire road map you should take in order to become a computer vision engineer with very specific resources and i have also showed you all the different ways in which you can specialize as a computer vision engineer Now let's continue because now it's time to show you how to enhance your skill sets, 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 having some 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, 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 skill sets 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 in 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 very advanced lesson And these are a few resources you could take in order to enhance your skillsets as a computer vision engineer And then you also have other resources and in these resources are for example books You could read books on computer vision in order to improve your skill sets 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 I 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 will consider computer vision machine learning these are some super high level some 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 civil diffusion i have used this subreddit I love 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 skillset a step further for sure. And 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.