Unsupervised Learning

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In unsupervised learning, algorithms are trained using unlabeled data. The system tries to learn patterns, structures, and relationships from the data on its own, without explicit guidance on what the "correct" output should be.

Key Characteristics:

• Input data is unlabeled.
• Used for discovery tasks (finding hidden patterns).
• Common tasks include Clustering, Dimensionality Reduction, and Association Rule Mining.

Examples:

• Clustering: Customer segmentation, grouping similar documents.
• Dimensionality Reduction: Principal Component Analysis (PCA) for feature reduction.
• Association Rule Mining: Market basket analysis (e.g., "customers who bought X also bought Y").

You can add more details on algorithms like K-Means, Hierarchical Clustering, PCA, Apriori, etc.