Unsupervised learning is a type of machine learning that focuses on training algorithms using data without labeled outputs. In this approach, the model identifies patterns, structures, and relationships within the input data, allowing it to group similar data points or extract meaningful features without human intervention. Common techniques used in unsupervised learning include clustering, dimensionality reduction, and association analysis. Applications of unsupervised learning are widespread, from customer segmentation in marketing to anomaly detection in fraud detection. By uncovering hidden patterns and insights in unlabeled data, unsupervised learning plays a vital role in exploratory data analysis and understanding complex datasets.