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 on 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 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).