Unsupervised Learning is a type of machine learning where the model is trained on unlabeled data. Unlike supervised learning, there are no predefined output labels, and the goal is to uncover hidden patterns, relationships, or structures within the data. Common unsupervised learning techniques include clustering, such as k-means and hierarchical clustering, and dimensionality reduction, such as PCA (Principal Component Analysis) and t-SNE (t-Distributed Stochastic Neighbor Embedding). Unsupervised learning is widely used for exploratory data analysis, anomaly detection, and data preprocessing, helping to identify natural groupings and simplify complex datasets.