Unsupervised Learning

Unsupervised learning is a type of machine learning algorithm that learns patterns from untagged data. In unsupervised learning, the algorithm is not given any specific output variables or target labels. Instead, it is left to explore the data and find structure, relationships, or patterns on its own.

This contrasts with supervised learning, where the algorithm is trained on a labeled dataset with known inputs and corresponding outputs, and reinforcement learning, where an agent learns through trial and error by receiving rewards or penalties.

Common tasks in unsupervised learning include clustering, dimensionality reduction, and anomaly detection.

Clustering is a fundamental unsupervised learning technique used to group similar data points together into clusters. The goal is to identify inherent groupings within the data such that data points within the same cluster are more similar to each other than to those in other clusters.

Algorithms like K-Means, Hierarchical Clustering, and DBSCAN are commonly used for this purpose.

Dimensionality reduction is the process of reducing the number of random variables or features under consideration, by obtaining a set of principal variables. This is done to simplify models, reduce computational cost, and avoid the "curse of dimensionality" while retaining as much relevant information as possible.

Techniques include Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE).

Anomaly detection (also known as outlier detection) is the identification of rare items, events, or observations that raise suspicions by differing significantly from the majority of the data. In unsupervised learning, anomalies are detected as data points that do not belong to any cluster or are far from the typical data distribution.

Applications include fraud detection, intrusion detection, and identifying defective products.