Unsupervised learning is a type of machine learning where algorithms are trained on data that is not labeled or classified. Unlike supervised learning, where the model learns from input-output pairs, unsupervised learning focuses on identifying patterns and structures within the data itself. This approach is particularly useful for tasks such as clustering, dimensionality reduction, and anomaly detection. By analyzing the inherent relationships in the data, unsupervised learning can reveal insights and group similar observations, making it a powerful tool for data exploration and analysis in various fields, including marketing, finance, and healthcare.