Topic 7. Unsupervised Learning: Principal Component Analysis and Clustering

Here we turn to the vast topic of unsupervised learning; it’s about the cases when we have data but it is unlabeled with no target feature to predict like in classification/regression tasks. Most of the data out there is unlabeled, and we need to be able to make use of it. We discuss only 2 types of unsupervised learning tasks – clustering and dimensionality reduction.

Steps in this block

1. Read the article (same in a form of a Kaggle Notebook);

2. Watch a video lecture series coming in 2 parts:

• Principal Component Analysis;

• Clustering;

3. Complete demo assignment 7 (same as a Kaggle Notebook) where you analyze data coming from mobile phone accelerometers and gyroscopes to cluster people into different types of physical activities;

4. Check out the solution (same as a Kaggle Notebook) to the demo assignment (optional);

5. Complete Bonus Assignment 7, where we walk you through Sklearn built-in implementations of dimensionality reduction and clustering methods and apply these techniques to the popular “faces” dataset (optional, available under Patreon “Bonus Assignments” tier).