https://github.com/dair-ai/ML-YouTube-Courses
📺 ML YouTube CoursesAt 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:
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
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🔗 Link to Course
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
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🔗 Link to Course
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
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🔗 Link to Course
To learn the probabilistic paradigm of ML:
Reasoning about uncertainty
Continuous Variables
Sampling
Markov Chain Monte Carlo
Gaussian Distributions
Graphical Models
Tuning Inference Algorithms
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🔗 Link to Course
To learn some of the fundamentals of deep learning:
Introduction to Deep Learning
🔗 Link to Course
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
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🔗 Link to Course
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
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🔗 Link to Course
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
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🔗 Link to Course
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
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🔗 Link to Course
To learn the latest neural network based techniques for NLP:
Language Modeling
Efficiency tricks
Conditioned Generation
Structured Prediction
Model Interpretation
Advanced Search Algorithms
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🔗 Link to Course
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
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🔗 Link to Course
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
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🔗 Link to Course
To learn advanced concepts in NLP:
Attention Mechanisms
Transformers
BERT
Question Answering
Model Distillation
Vision + Language
Ethics in NLP
Commonsense Reasoning
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🔗 Link to Course
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
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🔗 Link to Course
To learn about concepts in geometric deep learning:
Learning in High Dimensions
Geometric Priors
Grids
Manifolds and Meshes
Sequences and Time Warping
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🔗 Link to Course
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
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🔗 Link to Course
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
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🔗 Link to Course
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
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.