/ML-YouTube-Courses

📺 A place to discover the latest machine learning courses on YouTube.

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📺 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

Introduction to Machine Learning (Tubingen)

The course serves as a basic introduction to machine learning and covers key concepts in regression, classification, optimization, regularization, clustering, and dimensionality reduction.

  • Linear regression
  • Logistic regression
  • Regularization
  • Boosting
  • Neural networks
  • PCA
  • Clustering ...

🔗 Link to Course

Statistical Machine Learning (Tubingen)

The course covers the standard paradigms and algorithms in statistical machine learning.

  • KNN
  • Bayesian decision theory
  • Convex optimization
  • Linear and ridge regression
  • Logistic regression
  • SVM
  • Random Forests
  • Boosting
  • PCA
  • Clustering ...

🔗 Link to Course

Practical Deep Learning for Coders (2020)

After finishing this course you will know:

  • How to train models that achieve state-of-the-art results
  • How to turn your models into web applications, and deploy them
  • Why and how deep learning models work, and how to use that knowledge to improve the accuracy, speed, and reliability of your models
  • The latest deep learning techniques that really matter in practice
  • How to implement stochastic gradient descent and a complete training loop from scratch
  • How to think about the ethical implications of your work, to help ensure that you're making the world a better place and that your work isn't misused for harm ...

🔗 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

Deep Learning (Tubingen)

This course introduces the practical and theoretical principles of deep neural networks.

  • Computation graphs
  • Activation functions and loss functions
  • Training, regularization and data augmentation
  • Basic and state-of-the-art deep neural network architectures including convolutional networks and graph neural networks
  • Deep generative models such as auto-encoders, variational auto-encoders and generative adversarial networks ...

🔗 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

CS224U: Natural Language Understanding

To learn the latest concepts in natural language understanding:

  • Grounded Langugage Understanding
  • Relation Extraction
  • Natural Language Inference (NLI)
  • NLU and Neural Information Extraction
  • Adversarial testing ...

🔗 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

Reinforcement Learning Lecture Series (DeepMind)

The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence.

  • Introduction to RL
  • Dynamic Programming
  • Model-free algorithms
  • Deep reinforcement learning ...

🔗 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

Self-Driving Cars (Tubingen)

Covers the most dominant paradigms of self-driving cars: modular pipeline-based approaches as well as deep-learning based end-to-end driving techniques.

  • Camera, lidar and radar-based perception
  • Localization, navigation, path planning
  • Vehicle modeling/control
  • Deep Learning
  • Imitation learning
  • Reinfocement learning

🔗 Link to Course


Reach out on Twitter if you have any questions.

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.

You can now find ML Course notes here.