G3PythonAdvancedAbout 180 min to complete
Clustering
Every model so far learned from labelled examples — you told it the right answer (species, survived, churned) and it learned to reproduce it. Clustering is different: it is unsupervised.
Notebook
About 180 minutes to complete
Enrol to read the notebook and download it to run.
What this module covers
Every model so far learned from labelled examples — you told it the right answer (species, survived, churned) and it learned to reproduce it. Clustering is different: it is unsupervised.
By the end of it
- Explain unsupervised learning and how clustering differs from classification
- Run k-means, interpret its centroids and inertia, and choose k with the elbow and silhouette methods
- Understand why scaling is essential for distance-based clustering
- Run and read hierarchical (agglomerative) clustering with a dendrogram
- Run DBSCAN to find arbitrarily-shaped clusters and label noise/outliers
- Evaluate clusterings with the silhouette score (no labels) and adjusted Rand index (when labels exist)
- Use PCA to reduce dimensions for 2-D cluster visualisation