K-means Clustering and Principal Component Analysis in 10 Minutes

There are 2 major kinds of machine learning models: supervised and unsupervised. In supervised learning, you have input data X and output data y, then the model finds a map from X to y. In unsupervised learning, you only have input data X. The goal of unsupervised learning varies: clustering observations in X, reducing the dimensionality of X, anomaly detection in X, etc. As supervised learning has been discussed extensively in Part 1 and Part 2 of the series, this story is focused on unsupervised learning.