A lot of Machine Learning (ML) I learned during my Ph.D. was from youtube. I didn't have a guide to do this effectively and thus here it is:

A complete guide to studying ML from youtube: 13 best and most recent ML courses available on YouTube. 👩‍🏫🧵⤵️

We will start with "Stanford CS229: Machine Learning" by Andrew Ng to start and learn the following ML concepts:

Linear & Logistic Regression,
Naive Bayes, SVMs, Kernels
Decision Trees, Introduction to Neural Networks
Debugging ML

A series of mini-lectures (~5 mins) covering various introductory topics in ML by Cassie Kozyrkov, covering:

Explainability in AI, Precession vs. Recall, Statistical Significance, Clustering and K-means, and finally, Ensemble models.

Beyond an AI genius, Andrej Karpathy is a brilliant teacher. His creative teaching methods make this intro to Neural Networks (NN): Zero to Hero makes one of the best ways to get introduced to NN.

"MIT: Deep Learning for Art, Aesthetics, and Creativity " covers the application of deep learning for art, aesthetics, and creativity, including Neural Abstractions, Efficient GANs, and explorations in AI for Creativity.