Statistical Learning with Python - Statistical Learning: 12.1 Principal Components

Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing Trevor Hastie, Professor of Statistics and Biomedical Data Sciences at Stanford University - https://statistics.stanford.edu/people/trevor-j-hastie Robert Tibshirani, Professor of Statistics and Biomedical Data Sciences at Stanford University - https://statistics.stanford.edu/people/robert-tibshirani Jonathan Taylor, Professor Statistics at Stanford University - https://statistics.stanford.edu/people/jonathan-taylor You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion. You can choose to take the course in R (https://www.edx.org/course/statistica) or in Python (https://www.edx.org/learn/data-analysis-statistics/stanford-university-statistical-learning-with-python) For more information about courses on Statistics, you can browse our Stanford Online Catalog: https://stanford.io/3QHRi72 0:00 Introduction 2:03 The Goals of Unsupervised Learning 3:06 The Challenge of Unsupervised Learning 4:33 Another advantage 7:09 Principal Components Analysis: details 8:25 PCA: example 9:27 Computation of Principal Components 10:13 Computation: continued 11:23 Geometry of PCA