I have released a free 10-day email course on making a career in computer vision, you can join the course here.
Who am I?
Alright before I start this post, you might be wondering who am I to teach you, or what merit do I have to give advice regarding career options in AI. So here’s a quick intro of me:
I’m Taha Anwar. An Applied Computer Vision scientist, besides running Bleed AI, I’ve also worked for the official OpenCV.org team and led teams to develop high-end technical content for their premium courses.
I’ve also published a number of technical tutorials (blog posts, videos) applications at Bleed AI and at LearnOpenCV, given talks at prominent universities and international events on computer vision. Also published a useful computer vision python module at PyPI. This year I’ve also started a youtube channel to reach more people.
Why Create this Course?
So I’ve been working in this field for a number of years and during my time I’ve taught and helped a lot of people from University Grads to engineers and researchers. So I created this course in order to help people interested in computer vision reach their desired outcomes, whether it’s landing a job, becoming a researcher, building projects as a hobby, or whatever it might be, this course will help you get that and it’ll show you an ideal path from start to finish to master the computer vision career roadmap.
It doesn’t matter what your background level is, the course is designed to cover an audience of all experience levels.
Here’s what you’ll learn inside this FREE course each day.
- Day 1 | The Ideal Learning Technique: On the first day, you will learn about the best approach from the two main approaches ( Top-Down, and Bottom-Up ) to learn and master computer vision easily and efficiently.
- Day 2 | Building the Required Background: On the second day, I’ll show you exactly how you can build the Mathematical & Computer Science background for computer vision and share some short high-quality, and really easy to go through free courses to help you learn the prerequisites.
- Day 3 | Learning High-Level Artificial Intelligence: From day 3, the exciting stuff will start as you will dive into learning AI. I will share some high-level resources about a broad overview of the field and will tell you why it is important before getting involved in specifics.
- Day 4 | Learning Image Processing and Classical Computer Vision: On the fourth day, I will share some personally evaluated high-quality resources on Image Processing, and Classical Computer Vision and will explain why these techniques should be learned first before jumping into Deep learning.
- Day 5 | Learning the Theory behind AI/ML & Start Building Models: On the fifth day, finally, It will be time to go deeper and learn the theoretical foundations behind AI/ML algorithms and also start training algorithms using a high-level library to kill the dryness. I will share the right resources to help you get through.
- Day 6 | Learning Deep Learning Theory & Start Building DL Models: On the sixth day, we will step up the game and get into deep learning with a solid plan on how and what to learn.
- Day 7 | Learning Model Deployment & Start Building Computer Vision Projects: On the seventh day, we will move towards productionizing models and I’ll also discuss how to start working on your own computer vision projects to build up your portfolio.
- Day 8 | Learning to Read Computer Vision Papers: On the eighth day, I will share the best tips, suggestions, and practices to get comfortable with reading the papers and will also discuss its significance over other resources.
- Day 9 | Picking a Path; Research, Development, or Domain Expertise: On the ninth day, I’ll show you the final step you need in your journey in order to implement your knowledge, move forward in career and start making money.
- Day 10 | FINAL Lesson, the Journey ENDS with Bonus Tips: On the last day, I will guide you further and provide you with some bonus tips to keep in mind. I will also share a few final learning resources.
Here’s a video summarizing the entire Course.
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