What is unsupervised learning?

Unsupervised learning involves training on data that has no labels. The model must discover hidden structure or patterns on its own. The two main tasks are clustering (grouping similar data points, e.g., K-Means) and dimensionality reduction (compressing data while preserving structure, e.g., PCA). It is useful when labeling data is expensive or impractical, such as customer segmentation, anomaly detection, and topic modeling.