Awesome Free Computer Vision Courses

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Computer Vision is becoming one of the interesting AI (Artificial Intelligence) application. Computer Vision is the field of AI which can help the machine to interprets and understands the real-world scenes in a visual manner.

Computer Vision has numerous applications but three of the most interesting uses cases have been in medicine, machine vision and autonomous vehicles. For example, in medicine, a vision model can help the radiologist to detect or diagnose diseases like tumours. In machine vision, the industry can use a visual model to detect or inspect a fault in a manufactured product. Autonomous vehicles such as self-driving car use computer vision to navigate the world, with the abilities to recognize traffic signs, pedestrian moving in the road, and other surrounding objects or obstacles.

0*4OR30Qmyliq2p297.jpeg Self driving car, photo by Bram Van Oost on Unsplash

All of such applications make it worth learning Computer Vision. And there has never been a time like today that you can find awesome learning resources for free. In this tutorial, I will list five courses that can help you get into Computer Vision.

Amazon Machine Learning University - Computer Vision

This is a free course which was originally taught to Amazon employees but the company later made it available on YouTube. This course has 28 videos, each video having an average length of 5 minutes. The course is available on YouTube. Slides, notebooks and datasets are available here.

1*WCWSQ7TdQA5qp1SNWk4Qdg.png Image: Course highlight/YouTube

Stanford CS231n Computer Vision Class

This is an amazing free course tough by Stanford instructors such as famous Researcher and instructor Fei-Fei Li.

Screen Shot 2020-12-20 at 12.12.37 PM.png Image: Course highlight/YouTube

The course is entirely on Convolutional Neural networks (CNN) which is a fundamental block of modern Computer vision algorithms. The course is available on YouTube, and the slides are available here.

Deep Learning Specialization, Computer Vision

This is no doubt one of the best foundational course we have in deep learning today. The specialization has five courses which are neural networks and deep learning, improving deep neural networks: hyper-parameter tuning, regularization and optimization, structuring machine learning projects, convolutional neural networks and sequence models. The fourth course focuses on computer vision with convolutional neural networks.

1*STZVv5G5Q3HhEAYZ_oqWvg.png Image: Course highlight/YouTube

It was taught by legendary and one of the most AI influencers in the world, Andrew Ng. , co-founder of DeepLearning.AI which has the goal of making AI education available to almost everyone in the world. This course is available on YouTube. Check out DeepLearning.AI for more AI courses, and if you are interested in certificate and access to assignments, check out the course on Coursera.

Intro to Deep Learning by MIT, Deep Computer Vision

MIT Intro to Deep Learning also is a great course which is available for free on YouTube. It is an intensive series of lectures, taught in a single week in January 2020. One of the best things about this course series is how fast they are and straight to the point. Just in about 40 minutes, you get to learn the foundation of deep learning. youtube.com/watch?v=iaSUYvmCekI&list=PL..

1*mIRy0TY0OCZ5bH3UydeGpA.png Image: Course highlight/YouTube

One of the fun thing about this course is the welcome given by Barack Obama in [course one] (youtube.com/watch?v=njKP3FqW3Sk&list=PL..), but that was generated using Generative Adversarial Networks (GANs).

Kaggle Computer Vision Course

If you spend most of your time on Kaggle, you have probably seen that it has free courses. One of these courses in our interests now is Computer Vision. The course uses TensorFlow and Keras, and in tutorials and exercises provided, you can learn convolutional neural networks. Like said, the course is available on Kaggle.


Thanks for reading, and I hope you find these courses useful in your journey to become a vision master.

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