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.
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.
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Machine Learning (Stanford CS229) | Course website
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.
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Convolutional Neural Networks for Visual Recognition (Stanford CS231n) | Course website
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.
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Introduction to Artificial Intelligence (UC Berkeley CS188) | Course website
Covers the whole field of AI. From search methods, game trees and machine learning to Bayesian networks and reinforcement learning.
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Applied Machine Learning 2020 (Columbia)
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.
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Introduction to Reinforcement learning with David Silver (DeepMind) | Course website
Introduction to reinforcement learning by one of the leading researchers behind AlphaGo and AlphaZero.
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Natural Language Processing with Deep Learning (Stanford CS224N) | Course website
Modern NLP techniques from recurrent neural networks and word embeddings to transformers and self-attention. Covers applied topics like questions answering and text generation.
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Deep Learning - NYU - 2020 | Course website
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.
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Machine Learning with Graphs (Stanford CS224W) | Course website
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 transformers, and scaling GNNs to large graphs.
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Deep Unsupervised Learning (UC Berkeley CS294) | Course website
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Frontiers of Deep Learning (Simons Institute) | Course website
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Geometry of Deep Learning (Microsoft Research) | Course website
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Deep Multi-Task and Meta Learning (Stanford CS330) Autumn 2022 | Course Website
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Mathematics of Machine Learning Summer School 2019 (University of Washington) | Course Website
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Probabilistic Graphical Models (Carneggie Mellon University) | Course Website
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Probabilistic and Statistical Machine Learning 2020 (University of Tübingen)
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Advanced Deep Learning & Reinforcement Learning 2020 (DeepMind / UCL)
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Deep Reinforcment Learning, Decision Making and Control (UC Berkeley CS285)
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Stanford CS224U: Natural Language Understanding | Spring 2019
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New Directions in Reinforcement Learning and Control (Institure for Advanced Study)
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Workshop on Theory of Deep Learning: Where next (Institure for Advanced Study)
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Deep Learning: Alchemy or Science? (Institure for Advanced Study)
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Theoretical Machine Learning Lecture Series (Institure for Advanced Study)
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Introduction to Data-Centric AI (MIT) | Lecture videos | Lab assignments
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Transformers as a Computational Model (UC Berkeley, Simons Institute)
Advanced courses that require prior knowledge in machine learning and AI.