Clustering and dimensionality reduction
Unsupervised learning involves finding patterns in data without labeled responses. Clustering groups similar data points together, while dimensionality reduction reduces the number of features while preserving important information. Key clustering algorithms include K-Means, DBSCAN, and hierarchical clustering. Dimensionality reduction techniques include Principal Component Analysis (PCA) and t-SNE. Unsupervised learning is useful for exploratory data analysis, customer segmentation, anomaly detection, and data preprocessing. Evaluation of unsupervised learning is more challenging than supervised learning since there are no ground truth labels. Techniques include silhouette scores for clustering and reconstruction error for dimensionality reduction. Understanding unsupervised learning allows you to discover hidden patterns in data and prepare data for other machine learning tasks.