ML YouTube Courses
At DAIR.AI we open education. In this repo, we share some of the best and most recent machine learning courses available on YouTube.
Machine Learning
Deep Learning
NLP
Computer Vision
Reinforcement Learning
Graph ML
Multi-Task Learning
Others
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 ...
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 ...
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 ...
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 ...
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 ...
Introduction to Machine Learning (Munich)
Covers many supervised machine learning concepts.
Machine Learning Basics
Supervised Regression and Classification
Performance Evaluation
Classification and Regression Trees (CART)
Information Theory
Linear and Nonlinear Support Vector Machine
Gaussian Processes
...
Link to Course(YouTube links are embedded within the chapters)
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 ...
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 ...
Machine Learning with Graphs (Stanford)
To learn some of the latest graph techniques in machine learning:
PageRank
Matrix Factorizing
Node Embeddings
Graph Neural Networks
Knowledge Graphs
Deep Generative Models for Graphs ...
Probabilistic Machine Learning
To learn the probabilistic paradigm of ML:
Reasoning about uncertainty
Continuous Variables
Sampling
Markov Chain Monte Carlo
Gaussian Distributions
Graphical Models
Tuning Inference Algorithms ...
Introduction to Deep Learning
To learn some of the fundamentals of deep learning:
Introduction to Deep Learning
Deep Learning: CS 182
To learn some of the widely used techniques in deep learning:
Machine Learning Basics
Error Analysis
Optimization
Backpropagation
Initialization
Batch Normalization
Style transfer
Imitation Learning ...
Deep Unsupervised Learning
To learn the latest and most widely used techniques in deep unsupervised learning:
Autoregressive Models
Flow Models
Latent Variable Models
Self-supervised learning
Implicit Models
Compression ...
NYU Deep Learning SP21
To learn some of the advanced techniques in deep learning:
Neural Nets: rotation and squashing
Latent Variable Energy-Based Models
Unsupervised Learning
Generative Adversarial Networks
Autoencoders ...
Deep Learning (Tübingen)
This course introduces the practical and theoretical principles of deep neural networks.
Computation graphs
Activation functions and loss functions
Training, regularization, and data augmentation
Basic and state-of-the-art deep neural network architectures including convolutional networks and graph neural networks
Deep generative models such as auto-encoders, variational auto-encoders, and generative adversarial networks ...
Stanford CS25 - Transformers United
This course consists of lectures focused on Transformers, providing a deep dive and their applications
Introduction to Transformers
Transformers in Language: GPT-3, Codex
Applications in Vision
Transformers in RL & Universal Compute Engines
Scaling transformers
Interpretability with transformers ...
NLP Course (Hugging Face)
Learn about different NLP concepts and how to apply language models and Transformers to NLP:
What is Transfer Learning?
BPE Tokenization
Batching inputs
Fine-tuning models
Text embeddings and semantic search
Model evaluation
...
CS224N: Natural Language Processing with Deep Learning
To learn the latest approaches for deep learning based NLP:
Dependency parsing
Language models and RNNs
Question Answering
Transformers and pretraining
Natural Language Generation
T5 and Large Language Models
Future of NLP ...
CMU Neural Networks for NLP
To learn the latest neural network-based techniques for NLP:
Language Modeling
Efficiency tricks
Conditioned Generation
Structured Prediction
Model Interpretation
Advanced Search Algorithms ...
CS224U: Natural Language Understanding
To learn the latest concepts in natural language understanding:
Grounded Language Understanding
Relation Extraction
Natural Language Inference (NLI)
NLU and Neural Information Extraction
Adversarial testing ...
CMU Advanced NLP
To learn:
Basics of modern NLP techniques
Multi-task, Multi-domain, multi-lingual learning
Prompting + Sequence-to-sequence pre-training
Interpreting and Debugging NLP Models
Learning from Knowledge-bases
Adversarial learning ...
Multilingual NLP
To learn the latest concepts for doing multilingual NLP:
Typology
Words, Part of Speech, and Morphology
Advanced Text Classification
Machine Translation
Data Augmentation for MT
Low Resource ASR
Active Learning ...
Advanced NLP
To learn advanced concepts in NLP:
Attention Mechanisms
Transformers
BERT
Question Answering
Model Distillation
Vision + Language
Ethics in NLP
Commonsense Reasoning ...
Deep Learning for Computer Vision
To learn some of the fundamental concepts in CV:
Introduction to deep learning for CV
Image Classification
Convolutional Networks
Attention Networks
Detection and Segmentation
Generative Models ...
Deep Learning for Computer Vision (DL4CV)
To learn modern methods for computer vision:
CNNs
Advanced PyTorch
Understanding Neural Networks
RNN, Attention and ViTs
Generative Models
GPU Fundamentals
Self-Supervision
Neural Rendering
Efficient Architectures
AMMI Geometric Deep Learning Course
To learn about concepts in geometric deep learning:
Learning in High Dimensions
Geometric Priors
Grids
Manifolds and Meshes
Sequences and Time Warping ...
Deep Reinforcement Learning
To learn the latest concepts in deep RL:
Intro to RL
RL algorithms
Real-world sequential decision making
Supervised learning of behaviors
Deep imitation learning
Cost functions and reward functions ...
Reinforcement Learning Lecture Series (DeepMind)
The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence.
Introduction to RL
Dynamic Programming
Model-free algorithms
Deep reinforcement learning ...
Full Stack Deep Learning
To learn full-stack production deep learning:
ML Projects
Infrastructure and Tooling
Experiment Managing
Troubleshooting DNNs
Data Management
Data Labeling
Monitoring ML Models
Web deployment ...
Introduction to Deep Learning and Deep Generative Models
Covers the fundamental concepts of deep learning
Single-layer neural networks and gradient descent
Multi-layer neural networks and backpropagation
Convolutional neural networks for images
Recurrent neural networks for text
Autoencoders, variational autoencoders, and generative adversarial networks
Encoder-decoder recurrent neural networks and transformers
PyTorch code examples
Link to Course Link to Materials
Self-Driving Cars (Tübingen)
Covers the most dominant paradigms of self-driving cars: modular pipeline-based approaches as well as deep-learning based end-to-end driving techniques.
Camera, lidar, and radar-based perception
Localization, navigation, path planning
Vehicle modeling/control
Deep Learning
Imitation learning
Reinforcement learning
Reinforcement Learning (Polytechnique Montreal, Fall 2021)
Designing autonomous decision-making systems is one of the longstanding goals of Artificial Intelligence. Such decision-making systems, if realized, can have a big impact in machine learning for robotics, game playing, control, and health care to name a few. This course introduces Reinforcement Learning as a general framework to design such autonomous decision-making systems.
Introduction to RL
Multi-armed bandits
Policy Gradient Methods
Contextual Bandits
Finite Markov Decision Process
Dynamic Programming
Policy Iteration, Value Iteration
Monte Carlo Methods
...
Link to Course Link to Materials
Foundations of Deep RL
A mini 6-lecture series by Pieter Abbeel.
MDPs, Exact Solution Methods, Max-ent RL
Deep Q-Learning
Policy Gradients and Advantage Estimation
TRPO and PPO
DDPG and SAC
Model-based RL
Stanford CS234: Reinforcement Learning
Covers topics from basic concepts of Reinforcement Learning to more advanced ones:
Markov decision processes & planning
Model-free policy evaluation
Model-free control
Reinforcement learning with function approximation & Deep RL
Policy Search
Exploration
...
Link to Course Link to Materials
Stanford CS330: Deep Multi-Task and Meta-Learning
This is a graduate-level course covering different aspects of deep multi-task and meta learning.
Multi-task learning, transfer learning basics
Meta-learning algorithms
Advanced meta-learning topics
Multi-task RL, goal-conditioned RL
Meta-reinforcement learning
Hierarchical RL
Lifelong learning
Open problems
Link to Course Link to Materials
Advanced Robotics: UC Berkeley
This is course is from Peter Abbeel and covers a review on reinforcement learning and continues to applications in robotics.
MDPs: Exact Methods
Discretization of Continuous State Space MDPs
Function Approximation / Feature-based Representations
LQR, iterative LQR / Differential Dynamic Programming
...
Link to Course Link to Materials
If you are interested to contribute, feel free to open a PR with a link to the course. It will take a bit of time, but I have plans to do many things with these individual lectures. We can summarize the lectures, include notes, provide additional reading material, including the difficulty of content, etc.
You can now find ML Course notes here.