[Verse 1] When labels disappear and patterns hide in plain sight No teacher guides your algorithm through the night Customer behaviors swirl in multidimensional space Clustering reveals the tribes without a trace K-means divides with centroids as anchors DBSCAN finds the density, ignores the wankers [Chorus] Unsupervised, we excavate the buried gold Silhouette scores and elbow plots unfold PCA compresses while t-SNE maps the view When structure's what you're hunting, here's what algorithms do Cluster, reduce, detect what doesn't fit Intrinsic metrics tell you when you've nailed it [Verse 2] Hierarchical builds dendrograms like family trees Gaussian mixtures blend probabilities with ease Dimensionality reduction saves computational breath PCA preserves variance, UMAP conquers death Of high-dimensional curse that plagues machine learning souls While autoencoders reconstruct anomalous holes [Chorus] Unsupervised, we excavate the buried gold Silhouette scores and elbow plots unfold PCA compresses while t-SNE maps the view When structure's what you're hunting, here's what algorithms do Cluster, reduce, detect what doesn't fit Intrinsic metrics tell you when you've nailed it [Bridge] Isolation Forest isolates the odd ones out Local Outlier Factor leaves no statistical doubt Davies-Bouldin index measures cluster separation clean Association rules reveal what shopping baskets really mean Market basket analysis connects the dots between Products that dance together in consumer buying scenes [Verse 3] E-commerce segmentation splits customers in tribes Geographic, demographic, behavioral vibes UMAP visualization paints the clustering tale When ground truth is absent, intrinsic methods never fail Extrinsic validation needs external comparison But silhouette and cohesion judge each cluster's garrison [Chorus] Unsupervised, we excavate the buried gold Silhouette scores and elbow plots unfold PCA compresses while t-SNE maps the view When structure's what you're hunting, here's what algorithms do Cluster, reduce, detect what doesn't fit Intrinsic metrics tell you when you've nailed it [Outro] No labels needed when the data speaks its mind Unsupervised learning leaves no pattern left behind
← Unit 2.1 — Supervised Learning | Unit 2.3 — Reinforcement Learning →