Example Concept: In supervised learning, algorithms like linear regression, decision trees, and support vector machines use labeled datasets to learn the mapping from inputs to outputs, optimizing for accuracy in predictions. Unsupervised learning, on the other hand, employs techniques such as clustering (e.g., k-means, hierarchical clustering) and dimensionality reduction (e.g., PCA, t-SNE) to uncover hidden structures or relationships within data, often used for exploratory data analysis or feature extraction.