Unsupervised learning is a type of machine learning where an algorithm is trained on an unlabeled dataset, and the goal is to discover patterns, structures, or relationships within the data without explicit guidance or labeled outputs. Unlike supervised learning, there are no predefined target labels for the algorithm to predict during training. Instead, the algorithm explores the inherent structure of the data, identifying similarities, clusters, or hidden patterns. Unsupervised learning is often used for tasks such as clustering, dimensionality reduction, and association rule mining.

Common types of unsupervised learning techniques include:

• Clustering: Grouping similar data points together based on certain features or similarities. K-means clustering and hierarchical clustering are examples of clustering algorithms.

• Dimensionality Reduction: Reducing the number of features or variables in the data while preserving its essential information. Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) are common dimensionality reduction techniques.

• Association Rule Mining: Discovering relationships and associations between variables in the data. Apriori algorithm is an example often used for mining association rules in transactional datasets.

Unsupervised learning is valuable when dealing with large and complex datasets where the underlying structure is not explicitly known. It can help uncover hidden patterns that may not be immediately apparent and is widely used in various applications, including anomaly detection, data preprocessing, and exploratory data analysis.