Module 11- Theory: Eigenvalues, Eigenvectors and Principle Component Analysis (PCA and Kernel PCA)

Relevant playlists: Machine Learning Codes and Concepts: https://youtube.com/playlist?list=PL2GWo47BFyUNeLIH127rVovSqKFm1rk07&si=lCPyHenEQYBCJzQ_ Deep Learning Concepts, simply explained: https://www.youtube.com/playlist?list=PL2GWo47BFyUO6Fiy2mJCxR8sUrBEfT6BM Instructor: Pedram Jahangiry All of the slides and notebooks used in this series are available on my GitHub page, so you can follow along and experiment with the code on your own. https://github.com/PJalgotrader Lecture Outline: 0:00 intro and roadmap 10:43 Part 1: Unsupervised ML and PC terminology 15:30 Eigenvectors and Eigenvalues 28:34 Connecting PC and eigenthings 37:15 Part 2: pca, proportion variance explained and scree plot 56:05 Part 3: Pros and cons of PCA 1:00:58 Kernel trick and kernel PCA 1:14:17 Applications of PCA