Resources "Passive" learning opportunities

Machine Learning is such a hot topic these days, affecting almost every other field of research, that there are endless free resources online on all topics covered in this course. Everyone has a different learning style. I have provided some examples that I particularly like here, including YouTube videos, recordings of other university lectures, and visual tutorials, explained at all levels of difficulty and mathematical rigor.

Module: Introduction to Machine Learning

StatQuest with Josh Starmer YouTube channel

StatQuest with Josh Starmer YouTube channel

Machine Learning for Absolute Beginners by Oliver Theobald

Machine Learning for Absolute Beginners by Oliver Theobald

StatQuest Illustrated Guide to Machine Learning by Josh Starmer

StatQuest Illustrated Guide to Machine Learning by Josh Starmer

Mathematical Foundations of Machine Learning by Seongjai Kim

Mathematical Foundations of Machine Learning by Seongjai Kim

Serrano Academy

Serrano Academy

Module: Python Coding for Machine Learning

Microsoft's ML for Beginners GitHub course

Microsoft’s ML for Beginners GitHub course

Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka

Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka

Scikit-Learn Tutorials

Scikit-Learn Tutorials

Stanford CS231N's NumPy Tutorial

Stanford CS231N’s NumPy Tutorial

Google Colab Tutorials

Google Colab Tutorials

Kaggle Winning Solutions

Kaggle Winning Solutions

Module: Fundamental Learning Algorithms

Introduction to Machine Learning by Alex Smola

Introduction to Machine Learning by Alex Smola

Simplilearn Machine Learning playlist

Simplilearn Machine Learning playlist

Mathematics for Machine Learning by Marc Peter Deisenroth

Mathematics for Machine Learning by Marc Peter Deisenroth

Stanford CS229 lecture videos

Stanford CS229 lecture videos

Module: Practical Topics

Computer Vision: Foundations and Applications, compiled by Ranjay Krishna

Computer Vision: Foundations and Applications, compiled by Ranjay Krishna

Deep Learning for Natural Language Processing by Jason Brownlee

Deep Learning for Natural Language Processing by Jason Brownlee

Interpretable Machine Learning by Christoph Molnar

Interpretable Machine Learning by Christoph Molnar

Fairness and Machine Learning by Barocas, Hardt, and Narayanan

Fairness and Machine Learning by Barocas, Hardt, and Narayanan

Module: Deep Learning

Deep Learning with Python by Francois Chollet

Deep Learning with Python by Francois Chollet

NYU Deep Learning

NYU Deep Learning

MIT Deep Learning

MIT Deep Learning

TensorFlow tutorials

TensorFlow tutorials

PyTorch tutorials

PyTorch tutorials