🔍 Unsupervised Learning

Unsupervised learning helps us find structure in unlabeled data — a powerful way to uncover patterns, detect outliers, and reduce complexity in high-dimensional spaces.

This section teaches you how to:

• ✅ Apply clustering methods like K-Means, Hierarchical, and DBSCAN
  
• ✅ Use dimensionality reduction (PCA, t-SNE, UMAP) to visualize and simplify data
  
• ✅ Combine these methods to reveal hidden structures in real datasets
  
• ✅ Evaluate clustering results using metrics like silhouette score and adjusted Rand index (ARI)

💡 In unsupervised learning, there are no ground-truth labels — so the goal shifts from prediction to understanding structure and relationships in the data.

This section is especially valuable for domains like bioinformatics, customer segmentation, and anomaly detection.

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