Example Concept: In supervised learning, algorithms such as linear regression, decision trees, and neural networks are used to predict outcomes based on input features, leveraging labeled datasets to minimize prediction error. In contrast, unsupervised learning employs techniques like clustering (e.g., K-means, hierarchical clustering) and dimensionality reduction (e.g., PCA, t-SNE) to discover hidden patterns or groupings in data without any prior labels, often used for exploratory data analysis and feature extraction.