📺 ML YouTube Courses

At DAIR.AI we ❤️ open AI education. In this repo, we index and organize some of the best and most recent machine learning courses available on YouTube.

Machine Learning

• Caltech CS156: Learning from Data
• Stanford CS229: Machine Learning
• Making Friends with Machine Learning
• Applied Machine Learning
• Introduction to Machine Learning (Tübingen)
• Machine Learning Lecture (Stefan Harmeling)
• Statistical Machine Learning (Tübingen)
• Probabilistic Machine Learning
• MIT 6.S897: Machine Learning for Healthcare (2019)

Deep Learning

• Neural Networks: Zero to Hero
• MIT: Deep Learning for Art, Aesthetics, and Creativity
• Stanford CS230: Deep Learning (2018)
• Introduction to Deep Learning (MIT)
• CMU Introduction to Deep Learning (11-785)
• Deep Learning: CS 182
• Deep Unsupervised Learning
• NYU Deep Learning SP21
• Foundation Models
• Deep Learning (Tübingen)

Scientific Machine Learning

• Parallel Computing and Scientific Machine Learning

Practical Machine Learning

• LLMOps: Building Real-World Applications With Large Language Models
• Evaluating and Debugging Generative AI
• ChatGPT Prompt Engineering for Developers
• LangChain for LLM Application Development
• LangChain: Chat with Your Data
• Building Systems with the ChatGPT API
• LangChain & Vector Databases in Production
• Building LLM-Powered Apps
• Full Stack LLM Bootcamp
• Full Stack Deep Learning
• Practical Deep Learning for Coders
• Stanford MLSys Seminars
• Machine Learning Engineering for Production (MLOps)
• MIT Introduction to Data-Centric AI

Natural Language Processing

• XCS224U: Natural Language Understanding (2023)
• Stanford CS25 - Transformers United
• NLP Course (Hugging Face)
• CS224N: Natural Language Processing with Deep Learning
• CMU Neural Networks for NLP
• CS224U: Natural Language Understanding
• CMU Advanced NLP 2021/2022/2024
• Multilingual NLP
• Advanced NLP

Computer Vision

• CS231N: Convolutional Neural Networks for Visual Recognition
• Deep Learning for Computer Vision
• Deep Learning for Computer Vision (DL4CV)
• Deep Learning for Computer Vision (neuralearn.ai)

Reinforcement Learning

• Deep Reinforcement Learning
• Reinforcement Learning Lecture Series (DeepMind)
• Reinforcement Learning (Polytechnique Montreal, Fall 2021)
• Foundations of Deep RL
• Stanford CS234: Reinforcement Learning

Graph Machine Learning

• Machine Learning with Graphs (Stanford)
• AMMI Geometric Deep Learning Course

Multi-Task Learning

• Multi-Task and Meta-Learning (Stanford)

Others

• MIT Deep Learning in Life Sciences
• Self-Driving Cars (Tübingen)
• Advanced Robotics (Berkeley)

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Caltech CS156: Learning from Data

An introductory course in machine learning that covers the basic theory, algorithms, and applications.

• Lecture 1: The Learning Problem
• Lecture 2: Is Learning Feasible?
• Lecture 3: The Linear Model I
• Lecture 4: Error and Noise
• Lecture 5: Training versus Testing
• Lecture 6: Theory of Generalization
• Lecture 7: The VC Dimension
• Lecture 8: Bias-Variance Tradeoff
• Lecture 9: The Linear Model II
• Lecture 10: Neural Networks
• Lecture 11: Overfitting
• Lecture 12: Regularization
• Lecture 13: Validation
• Lecture 14: Support Vector Machines
• Lecture 15: Kernel Methods
• Lecture 16: Radial Basis Functions
• Lecture 17: Three Learning Principles
• Lecture 18: Epilogue

🔗 Link to Course

Stanford CS229: Machine Learning

To learn some of the basics of ML:

• Linear Regression and Gradient Descent
• Logistic Regression
• Naive Bayes
• SVMs
• Kernels
• Decision Trees
• Introduction to Neural Networks
• Debugging ML Models ...

🔗 Link to Course

Making Friends with Machine Learning

A series of mini lectures covering various introductory topics in ML:

• Explainability in AI
• Classification vs. Regression
• Precession vs. Recall
• Statistical Significance
• Clustering and K-means
• Ensemble models ...

🔗 Link to Course

Neural Networks: Zero to Hero (by Andrej Karpathy)

Course providing an in-depth overview of neural networks.

• Backpropagation
• Spelled-out intro to Language Modeling
• Activation and Gradients
• Becoming a Backprop Ninja

🔗 Link to Course

MIT: Deep Learning for Art, Aesthetics, and Creativity

Covers the application of deep learning for art, aesthetics, and creativity.

• Nostalgia -> Art -> Creativity -> Evolution as Data + Direction
• Efficient GANs
• Explorations in AI for Creativity
• Neural Abstractions
• Easy 3D Content Creation with Consistent Neural Fields ...

🔗 Link to Course

Stanford CS230: Deep Learning (2018)

Covers the foundations of deep learning, how to build different neural networks(CNNs, RNNs, LSTMs, etc...), how to lead machine learning projects, and career advice for deep learning practitioners.

• Deep Learning Intuition
• Adversarial examples - GANs
• Full-cycle of a Deep Learning Project
• AI and Healthcare
• Deep Learning Strategy
• Interpretability of Neural Networks
• Career Advice and Reading Research Papers
• Deep Reinforcement Learning

🔗 Link to Course 🔗 Link to Materials

Applied Machine Learning

To learn some of the most widely used techniques in ML:

• Optimization and Calculus
• Overfitting and Underfitting
• Regularization
• Monte Carlo Estimation
• Maximum Likelihood Learning
• Nearest Neighbours
• ...

🔗 Link to Course

Introduction to Machine Learning (Tübingen)

The course serves as a basic introduction to machine learning and covers key concepts in regression, classification, optimization, regularization, clustering, and dimensionality reduction.

• Linear regression
• Logistic regression
• Regularization
• Boosting
• Neural networks
• PCA
• Clustering
• ...

🔗 Link to Course

Machine Learning Lecture (Stefan Harmeling)

Covers many fundamental ML concepts:

• Bayes rule
• From logic to probabilities
• Distributions
• Matrix Differential Calculus
• PCA
• K-means and EM
• Causality
• Gaussian Processes
• ...

🔗 Link to Course

Statistical Machine Learning (Tübingen)

The course covers the standard paradigms and algorithms in statistical machine learning.

• KNN
• Bayesian decision theory
• Convex optimization
• Linear and ridge regression
• Logistic regression
• SVM
• Random Forests
• Boosting
• PCA
• Clustering
• ...

🔗 Link to Course

Practical Deep Learning for Coders

This course covers topics such as how to:

• Build and train deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering problems
• Create random forests and regression models
• Deploy models
• Use PyTorch, the world’s fastest growing deep learning software, plus popular libraries like fastai and Hugging Face
• Foundations and Deep Dive to Diffusion Models
• ...

🔗 Link to Course - Part 1

🔗 [Link to Cour