Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Awesome free machine learning and AI courses with video lectures.
| Date | Stars |
|---|---|
| 2026-07-24 | 3090 |
| 2026-07-25 | 3090 |
| 2026-07-28 | 3090 |
| 2026-07-30 | 3090 |
| 2026-07-31 | 3095 |
| 2026-08-06 | 3099 |
Today
+4 stars today
This week
+9 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.29%/day
# Awesome Machine Learning and AI Courses
A curated list of awesome, free machine learning and artificial intelligence courses
with video lectures.
All courses are available as high-quality video lectures by some of the best
AI researchers and teachers on this planet.
Besides the video lectures, I linked course websites with lecture notes,
additional readings and assignments.
## Introductory Lectures
These are great courses to get started in machine learning and AI.
No prior experience in ML and AI is needed. You should have some knowledge of
linear algebra, introductory calculus and probability.
Some programming experience is also recommended.
* [Machine Learning (Stanford CS229)](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) | [Course website](http://cs229.stanford.edu/syllabus-autumn2018.html)
This modern classic of machine learning courses is a great starting point
to understand the concepts and techniques of machine learning.
The course covers many widely used techniques,
The lecture notes are detailed and review necessary mathematical concepts.
* [Convolutional Neural Networks for Visual Recognition (Stanford CS231n)](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) | [Course website](https://cs231n.github.io/)
A great way to start with deep learning. The course focuses on
convolutional neural networks and computer vision, but also
gives an overview on recurrent networks and reinforcement learning.
* [Introduction to Artificial Intelligence (UC Berkeley CS188)](https://www.youtube.com/playlist?list=PL7k0r4t5c108AZRwfW-FhnkZ0sCKBChLH) | [Course website](https://inst.eecs.berkeley.edu/~cs188/fa18/index.html)
Covers the whole field of AI. From search methods, game trees and machine learning to Bayesian networks and reinforcement learning.
* [Applied Machine Learning 2020 (Columbia)](https://www.youtube.com/playlist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM)
Alternative to Stanford CS229. As the name implies, this course takes a more
applied perspective than Andrew Ng's machine learning lecture at Stanford.
You will see more code than mathematics. Concepts and algorithms are
using the popular Python libraries scikit-learn and Keras.
* [Introduction to Reinforcement learning with David Silver (DeepMind)](https://www.youtube.com/playlist?list=PLqYmG7hTraZBiG_XpjnPrSNw-1XQaM_gB) | [Course website](https://www.davidsilver.uk/teaching/)
Introduction to reinforcement learning by one of the leading researchers behind
AlphaGo and AlphaZero.
* [Natural Language Processing with Deep Learning (Stanford CS224N)](https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ) | [Course website](http://web.stanford.edu/class/cs224n/)
Modern NLP techniques from recurrent neural networks and word embeddings
to transformers and self-attention. Covers applied topics like questions answering and
text generation.
* [Deep Learning - NYU - 2020](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) | [Course website](https://atcold.github.io/pytorch-Deep-Learning/)
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.
* [Machine Learning with Graphs (Stanford CS224W)](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn) | [Course website](https://web.stanford.edu/class/cs224w/)
Comprehensive overview of machine learning techniques applied to graph-structured data. Topics include node embeddings, graph neural networks (GNNs), heterogeneous graphs, knowledge graphs, and their applications.
The course also covers advanced topics like neural subgraph matching, graph transfExcerpt of 9,015 characters
Read on GitHub
17
2
2
Anish Athalye · @cursor · United States
1
Mike · United States
1
Amr Kayid · ˈiː.zi · Canada
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b2270478c188fe81, topic:reinforcement-learning, readme:reinforcement learning
matched fp:b2270478c188fe81, topic:deep-learning