📺 ML YouTube Courses

At DAIR.AI we ❤️ open education. We are excited to share some of the best and most recent machine learning courses available on YouTube.

Course List:

• Stanford CS229: Machine Learning
• Applied Machine Learning
• Machine Learning with Graphs (Stanford)
• Probabilistic Machine Learning
• Introduction to Deep Learning (MIT)
• Deep Learning: CS 182
• Deep Unsupervised Learning
• NYU Deep Learning SP21
• CS224N: Natural Language Processing with Deep Learning
• CMU Neural Networks for NLP
• CMU Advanced NLP
• Multilingual NLP
• Advanced NLP
• Deep Learning for Computer Vision
• Deep Reinforcement Learning
• Full Stack Deep Learning
• AMMI Geometric Deep Learning Course (2021)

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

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

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

Introduction to Deep Learning

To learn some of the fundamentals of deep learning:

• Introduction to Deep Learning

🔗 Link to Course

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

CS224N: Natural Language Processing with Deep Learning

To learn the latest approaches for deep leanring 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

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 Course

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 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

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

AMMI Geometric Deep Learning Course (2021)

To learn about concepts in geometric deep learning:

• Learning in High Dimensions
• Geometric Priors
• Grids
• Manifolds and Meshes
• Sequences and Time Warping ...

🔗 Link to Course

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 ...

🔗 Link to Course

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 ...

🔗 Link to Course

Introduction to Deep Learning and Deep Generative Models

Covers the fundamental concepts of deep learning

• Single-layer neural networks and gradient descent
• Multi-layer neura 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

What's Next?

There are many plans to keep improving this collection. For instance, I will be sharing notes and better organizing individual lectures in a way that provides a bit of guidance for those that are getting started with machine learning.

If you are interested to contribute, feel free to open a PR with links to all individual lectures for each 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, include difficulty of content, etc.