Probability, Statistics, Data Science
Linear Algebra - Math for Machine Learning
Stanford CS224W: Machine Learning with Graphs
Daniel Bourke | Machine Learning Engineer
Making Friends with Machine Learning | Google
How I’d learn ML in 2024 (if I could start over)
Stanford CS229 ML - Andrew Ng - 2018
Fundamentals of Machine Learning | Complexity Explorer
Supervised Machine Learning: Regression and Classification
fast.ai — Making neural nets uncool again
Neural Networks and Deep Learning
Building a neural network FROM SCRATCH (no Tensorflow/Pytorch, just numpy & math)
Neural Networks from Scratch - An interactive guide to neural networks
The Complete Mathematics of Neural Networks and Deep Learning
Introduction to Reinforcement Learning with David Silver | Google DeepMind
The Ancient Secrets of Computer Vision - An Introduction to Computer Vision
Receive the TensorFlow Developer Certificate - TensorFlow
MIT Deep Learning and Artificial Intelligence Lectures | Lex Fridman
Geometric Deep Learning - Michael Bronstein, Petar Veličković
AI Interpretability Intro & Prereqs — Marco Molinari
Eliezer Yudkowsky explains AI interpretability | Lex Fridman Podcast
https://youtu.be/VMj-3S1tku0?si=TWVYsz7TTSsb6qSa
Neural Networks and Deep Learning - Coursera
PyTorch for Deep Learning & Machine Learning
https://github.com/erodola/NumMeth-s2-2023