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Unsupervised Learning
Unsupervised learning works with data that has no labeled outcomes. Instead of predicting a known answer, the model looks for inherent structure, patterns, or groupings within the data itself.
Main Categories
| Category | Goal | Example Algorithms |
|---|---|---|
| Clustering | Group similar data points together | K-Means, Hierarchical Clustering, DBSCAN |
| Dimensionality Reduction | Reduce the number of features while preserving information | PCA, t-SNE, UMAP |
| Association Rule Learning | Discover relationships between variables | Apriori, FP-Growth |
Clustering Explained
- K-Means: Partitions data into K clusters based on distance to cluster centroids.
- Hierarchical Clustering: Builds a tree of nested clusters, useful when the number of clusters isn't known upfront.
- DBSCAN: Groups points based on density, naturally handling noise and irregular cluster shapes.
Dimensionality Reduction Explained
Principal Component Analysis (PCA) transforms correlated features into a smaller set of uncorrelated components that capture most of the variance in the data — useful for visualization and speeding up downstream models.
Real-World Applications
- Customer segmentation for targeted marketing.
- Anomaly detection in network security.
- Market basket analysis (association rules) in retail.
- Visualizing high-dimensional data (e.g., gene expression data).
Key Takeaway: Unsupervised learning is invaluable when labels are unavailable or expensive to obtain — it lets the data reveal its own structure.
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