3 lessons
K-means clustering, finding groups nobody labeled
How a computer sorts data into groups with no answer key at all, using nothing but the distance formula from the math foundations module.
PCA, squashing many features into a few that matter
How principal component analysis finds the handful of directions in your data that carry almost all the real information, and drops the rest.
Ensembles, random forests, and boosting
Why combining many weak, imperfect models often beats any single strong one, using nothing more than averaging and voting.