Unsupervised Learning

Unsupervised Learning is a type of machine learning where models learn patterns and structures from data without the need for labeled responses. Unlike Supervised Learning, which relies on input-output pairs for training, unsupervised learning works with data that lacks specific labels or outcomes. The goal is to uncover the hidden structure or distribution within the data.

Examples: * Clustering - groups similar data points into clusters, where items within a cluster share more similarity with each other than with those in other clusters. * Dimensionality Reduction - to reduce the number of variables under consideration, simplifying the data while retaining essential information. * Anomaly Detection - focuses on identifying unusual data points that deviate significantly from the norm * Self-Supervised Learning - modifying the data in someway to create labels.