November 3, 2021

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A repository to index and organize the latest machine learning courses found on YouTube.

repo name dair-ai/ML-YouTube-Courses
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created 2021-06-25

πŸ“Ί ML YouTube Courses

At 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

To learn some of the basics of ML:

β€’ Linear Regression and Gradient Descent β€’ Logistic Regression β€’ Naive Bayes β€’ SVMs β€’ Kernels β€’ Decision Trees β€’ Introduction to Neural Networks β€’ Debugging ML Models …

πŸ”— Link to Course

Applied Machine Learning

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

β€’ Optimization and Calculus β€’ Overfitting and Underfitting β€’ Regularization β€’ Monte Carlo Estimation β€’ Maximum Likelihood Learning β€’ Nearest Neighbours …

πŸ”— Link to Course

Machine Learning with Graphs (Stanford)

To learn some of the latest graph techniques in machine learning:

β€’ PageRank β€’ Matrix Factorizing β€’ Node Embeddings β€’ Graph Neural Networks β€’ Knowledge Graphs β€’ Deep Generative Models for Graphs …

πŸ”— Link to Course

Probabilistic Machine Learning

To learn the probabilistic paradigm of ML:

β€’ Reasoning about uncertainty β€’ Continuous Variables β€’ Sampling β€’ Markov Chain Monte Carlo β€’ Gaussian Distributions β€’ Graphical Models β€’ Tuning Inference Algorithms …

πŸ”— Link to Course

Introduction to Deep Learning

To learn some of the fundamentals of deep learning:

β€’ Introduction to Deep Learning

πŸ”— Link to Course

Deep Learning: CS 182

To learn some of the widely used techniques in deep learning:

β€’ Machine Learning Basics β€’ Error Analysis β€’ Optimization β€’ Backpropagation β€’ Initialization β€’ Batch Normalization β€’ Style transfer β€’ Imitation Learning …

πŸ”— Link to Course

Deep Unsupervised Learning

To learn the latest and most widely used techniques in deep unsupervised learning:

β€’ Autoregressive Models β€’ Flow Models β€’ Latent Variable Models β€’ Self-supervised learning β€’ Implicit Models β€’ Compression …

πŸ”— Link to Course

NYU Deep Learning SP21

To learn some of the advanced techniques in deep learning:

β€’ Neural Nets: rotation and squashing β€’ Latent Variable Energy Based Models β€’ Unsupervised Learning β€’ Generative Adversarial Networks β€’ Autoencoders …

πŸ”— Link to Course

CS224N: Natural Language Processing with Deep Learning

To learn the latest approaches for deep leanring based NLP: β€’ Dependency parsing β€’ Language models and RNNs β€’ Question Answering β€’ Transformers and pretraining β€’ Natural Language Generation β€’ T5 and Large Language Models β€’ Future of NLP …

πŸ”— Link to Course

CMU Neural Networks for NLP

To learn the latest neural network based techniques for NLP: β€’ Language Modeling β€’ Efficiency tricks β€’ Conditioned Generation β€’ Structured Prediction β€’ Model Interpretation β€’ Advanced Search Algorithms …

πŸ”— Link to Course

Multilingual NLP

To learn the latest concepts for doing multilingual NLP:

β€’ Typology β€’ Words, Part of Speech, and Morphology β€’ Advanced Text Classification β€’ Machine Translation β€’ Data Augmentation for MT β€’ Low Resource ASR β€’ Active Learning …

πŸ”— Link to Course

Advanced NLP

To learn advanced concepts in NLP:

β€’ Attention Mechanisms β€’ Transformers β€’ BERT β€’ Question Answering β€’ Model Distillation β€’ Vision + Language β€’ Ethics in NLP β€’ Commonsense Reasoning …

πŸ”— Link to Course

Deep Learning for Computer Vision

To learn some of the fundamental concepts in CV:

β€’ Introduction to deep learning for CV β€’ Image Classification β€’ Convolutional Networks β€’ Attention Networks β€’ Detection and Segmentation β€’ Generative Models …

πŸ”— Link to Course

AMMI Geometric Deep Learning Course (2021)

To learn about concepts in geometric deep learning:

β€’ Learning in High Dimensions β€’ Geometric Priors β€’ Grids β€’ Manifolds and Meshes β€’ Sequences and Time Warping …

πŸ”— Link to Course

Deep Reinforcement Learning

To learn the latest concepts in deep RL:

β€’ Intro to RL β€’ RL algorithms β€’ Real-world sequential decision making β€’ Supervised learning of behaviors β€’ Deep imitation learning β€’ Cost functions and reward functions …

πŸ”— Link to Course

Full Stack Deep Learning

To learn full-stack production deep learning:

β€’ ML Projects β€’ Infrastructure and Tooling β€’ Experiment Managing β€’ Troubleshooting DNNs β€’ Data Management β€’ Data Labeling β€’ Monitoring ML Models β€’ Web deployment …

πŸ”— Link to Course

Introduction to Deep Learning and Deep Generative Models

Covers the fundamental concepts of deep learning

β€’ Single-layer neural networks and gradient descent β€’ Multi-layer neura networks and backpropagation β€’ Convolutional neural networks for images β€’ Recurrent neural networks for text β€’ autoencoders, variational autoencoders, and generative adversarial networks β€’ encoder-decoder recurrent neural networks and transformers β€’ PyTorch code examples

πŸ”— Link to Course πŸ”— 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.

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