Unsupervised learning has 2 main groups: – Clustering: Groups similar data points. – Dimensionality Reduction: Reduces complex data for easier analysis.
– K-Means: Partitions data into pre-defined groups (k). – Hierarchical Clustering: Builds a hierarchy of clusters. These algorithms find hidden groups in your data!
– Principal Component Analysis (PCA): Identifies the most important features. – Autoencoders: Learn a compressed representation of the data.
– Market Segmentation: Group customers based on buying habits. – Anomaly Detection: Identify unusual patterns in data. – Fraud Detection: Spot fraudulent transactions.