Lecture 24 – Clustering, Part 1
Presented by Isaac Schmidt
Content by Josh Hug
A reminder – the right column of the table below contains Quick Checks. These are not required but suggested to help you check your understanding.
| Video | Quick Check | |
|---|---|---|
| 24.0 Introduction. |
||
| 24.1 Introduction to clustering. Examples of clustering in practice. |
24.1 | |
| 24.2 The K-Means clustering algorithm. Example of K-Means clustering. |
24.2 | |
| 24.3 Loss functions for K-Means. Inertia and distortion. Optimizing inertia. |
24.3 | |
| 24.4 Agglomerative clustering as an alternative to K-Means. Example of agglomerative clustering. Dendrograms and other clustering algorithms. |
24.4 | |
| 24.5 Picking the number of clusters. The elbow method and silhouette scores. Summary of clustering and machine learning. |
24.5 |