Unsupervised learning is a machine learning paradigm where algorithms discover hidden patterns and structures in unlabeled data without predefined outputs or target variables. Unlike supervised learning, these methods work autonomously to identify similarities, groupings, and anomalies across clustering, dimensionality reduction, and anomaly detection tasks. The key insight: unsupervised algorithms must balance discovering meaningful structure while avoiding overfitting to noise, making evaluation metrics and domain knowledge essential for interpreting results.