📺 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
Deep Learning
Scientific Machine Learning
Practical Machine Learning
Natural Language Processing
Computer Vision
Reinforcement Learning
Graph Machine Learning
Multi-Task Learning
Others
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 Course - Part 2
Stanford MLSys Seminars
A seminar series on all sorts of topics related to building machine learning systems.
🔗 Link to Lectures
Machine Learning Engineering for Production (MLOps)
Specialization course on MLOPs by Andrew Ng.
🔗 Link to Lectures
MIT Introduction to Data-Centric AI
Covers the emerging science of Data-Centric AI (DCAI) that studies techniques to improve datasets, which is often the best way to improve performance in practical ML applications. Topics include:
- Data-Centric AI vs. Model-Centric AI
- Label Errors
- Dataset Creation and Curation
- Data-centric Evaluation of ML Models
- Class Imbalance, Outliers, and Distribution Shift
- ...
🔗 Course Website
🔗 Lecture Videos
🔗 Lab Assignments
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
- ...
🔗 Link to Course
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
- ...
🔗 Link to Course
MIT 6.S897: Machine Learning for Healthcare (2019)
This course introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows.
🔗 Link to Course
Introduction to Deep Learning
To learn some of the fundamentals of deep learning:
- Introduction to Deep Learning
🔗 Link to Course
CMU Introduction to Deep Learning (11-785)
The course starts off gradually from MLPs (Multi Layer Perceptrons) and then progresses into concepts like attention
and sequence-to-sequence models.
🔗 Link to Course
🔗 Lectures
🔗 Tutorials/Recitations
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
- ...
🔗 Link to Course
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
- ...
🔗 Link to Course
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
- ...
🔗 Link to Course
Foundation Models
To learn about foundation models like GPT-3, CLIP, Flamingo, Codex, and DINO.
🔗 Link to Course
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
- ...
🔗 Link to Course
Parallel Computing and Scientific Machine Learning
- The Basics of Scientific Simulators
- Introduction to Parallel Computing
- Continuous Dynamics
- Inverse Problems and Differentiable Programming
- Distributed Parallel Computing
- Physics-Informed Neural Networks and Neural Differential Equations
- Probabilistic Programming, AKA Bayesian Estimation on Programs
- Globalizing the Understanding of Models
🔗 Link to Course
XCS224U: Natural Language Understanding (2023)
This course covers topics such as:
- Contextual Word Representations
- Information Retrieval
- In-context learning
- Behavioral Evaluation of NLU models
- NLP Methods and Metrics
- ...
🔗 Link to Course
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
- ...
🔗 Link to Course
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
- ...
🔗 Link to Course
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
- ...
🔗 Link to Course
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
- ...
🔗 Link to Course
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
- ...
🔗 Link to Course
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
- ...
🔗 Link to 2021 Edition
🔗 Link to 2022 Edition
🔗 Link to 2024 Edition
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
- ...
🔗 Link to 2020 Course
🔗 Link to 2022 Course
Advanced NLP
To learn advanced concepts in NLP:
- Attention Mechanisms
- Transformers
- BERT
- Question Answering
- Model Distillation
- Vision + Language
- Ethics in NLP
- Commonsense Reasoning
- ...
🔗 Link to Course
CS231N: Convolutional Neural Networks for Visual Recognition
Stanford's Famous CS231n course. The videos are only available for the Spring 2017 semester. The course is currently known as Deep Learning for Computer Vision, but the Spring 2017 version is titled Convolutional Neural Networks for Visual Recognition.
- Image Classification
- Loss Functions and Optimization
- Introduction to Neural Networks
- Convolutional Neural Networks
- Training Neural Networks
- Deep Learning Software
- CNN Architectures
- Recurrent Neural Networks
- Detection and Segmentation
- Visualizing and Understanding
- Generative Models
- Deep Reinforcement Learning
🔗 Link to Course 🔗 Link to Materials
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
🔗 Link to Course
Deep Learning for Computer Vision (DL4CV)
To learn mod
A curated index maintained by DAIR.AI that organizes the best and most recent machine learning and AI courses available on YouTube, from intro ML to deep learning, NLP, and reinforcement learning.