Unsupervised Learning

Unsupervised learning involves training an algorithm using an unlabeled dataset, where the output (label) is not provided. The algorithm tries to identify patterns or structures in the data, such as grouping similar data points together or finding the underlying distribution of the data. Examples of unsupervised learning tasks include clustering (grouping data points based on similarity) and dimensionality reduction (reducing the number of features while preserving the structure of the data).