Unsupervised learning is a branch of machine learning where the model finds patterns and structure in data without labeled examples or human guidance. The most common tasks are clustering (grouping similar items) and dimensionality reduction (compressing features). It is useful when labels are expensive or impossible to obtain: a retailer discovers natural customer segments, a researcher identifies gene groups in biological data, and a security system detects unusual network behavior.