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Letter U
Unsupervised Learning

Unsupervised Learning is a machine learning paradigm where models learn patterns and relationships from unlabeled data without specific output labels. It focuses on finding hidden structures and patterns in data to make inferences and discover insights.

Use Cases

Clustering

Grouping similar data points into clusters based on patterns or features.

Anomaly Detection

Identifying unusual patterns or outliers in data without prior labels.

Dimensionality Reduction

Reducing the number of variables in data while preserving important information.

Importance

Exploratory Analysis

Provides insights into data structure and relationships for further analysis.

Scalability

Scales well with large datasets where labeled data may be scarce or costly.

Feature Discovery

Uncovers hidden patterns and structures that may not be apparent through supervised methods.

Analogies

Unsupervised Learning is like exploring a new city without a map or guide. Just as you discover patterns and locations by exploring streets and neighborhoods without predefined directions, unsupervised learning discovers patterns and relationships in data without labeled examples.

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