Module 11 · Introduction to Machine Learning

Unsupervised Learning: Clustering & Dimensionality Reduction: lessons

Finding groups without labels with K-means, and condensing many columns into a few with PCA.

3 lessons · 1h

What this module teaches, what it asks of you first and how you practise it are on its syllabus page.

Clustering with K-Means
20 min
Two steps on repeat — assign every point to its nearest centre, move each centre to the mean of its group — and you have the most widely used clustering algorithm.
Included with the track Simulation inside
Principal Component Analysis (PCA)
20 min
PCA finds the directions in which the data varies most and projects the data onto a few of them. Many correlated columns become a few uncorrelated components.
Included with the track Simulation inside
DBSCAN, Hierarchical Clustering and Gaussian Mixtures
20 min
Clustering for groups that are not round, and when to use each method.
Included with the track Simulation inside

Clustering with K-Means