Define unsupervised learning.

Unsupervised learning is defined as a type of machine learning where the model learns from unlabeled data. In this approach, the algorithm analyzes input data without any explicit instructions on what to look for, enabling it to discover inherent patterns, groupings, and relationships within the data. This self-guided learning process allows for tasks such as clustering, dimensionality reduction, and anomaly detection, providing insights that may not be immediately visible in labeled datasets.

For example, in clustering, unsupervised learning can identify distinct groups within data based on similarities or differences, such as grouping customers with similar purchasing behaviors or segmenting images based on color or structure without prior knowledge of the categories they belong to. Thus, unsupervised learning is particularly useful in cases where labeling data would be expensive or impractical.