Part 3: Clustering

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1. General Note: May be useful to add some content on dimensionality reduction (e.g., PCA, SVD, LCA).
2. K-Means Clustering: May want to touch upon the problem of determine the optimal number of clusters and some common heuristics used (e.g., elbow method, silhouette)
3. Agglomerative Clustering: Change the n_clusters parameter in the model from 3 to 2.
4. Agglomerative Clustering: In the visualization, we assign a label to the scatter plots, but never call plt.legend() to add the legend.
5. Challenge DBSCAN: When comparing the inferred clusters to the set of labels in the blobs data, we do not call len(set(labels)) to compare with the inferred number of clusters as we do in the moons data.