/ML-YouTube-Courses

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

📺 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 ...

🔗 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.