Unsupervised learning is a branch of machine learning that deals with training algorithms on data that is not labeled or classified. Unlike supervised learning, where the model learns from input-output pairs, unsupervised learning allows algorithms to identify patterns and structures within the data independently. This approach is particularly useful for tasks such as clustering, anomaly detection, and dimensionality reduction. By exploring the inherent relationships in the data, unsupervised learning can reveal insights that may not be immediately apparent, making it a powerful tool for data analysis and exploration in various fields, including data science and artificial intelligence.