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

Overview

πŸ“Ί 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

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 ...

πŸ”— 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 ...

πŸ”— 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 ...

πŸ”— 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 ...

πŸ”— Link to Course

Introduction to Deep Learning

To learn some of the fundamentals of deep learning:

  • Introduction to Deep Learning

πŸ”— 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 ...

πŸ”— 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 ...

πŸ”— 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 ...

πŸ”— 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 ...

πŸ”— 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 ...

πŸ”— 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 ...

πŸ”— 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 ...

πŸ”— 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 ...

πŸ”— 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 ...

πŸ”— 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 ...

πŸ”— 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 ...

πŸ”— 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 ...

πŸ”— 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

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.

Owner
DAIR.AI
Democratizing Artificial Intelligence Research, Education, and Technologies
DAIR.AI
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