Example Concept: In supervised learning, algorithms like linear regression, decision trees, and neural networks are used to predict outcomes based on input-output pairs. The model learns from the training data by minimizing the error between predicted and actual outcomes. Unsupervised learning, on the other hand, employs techniques such as clustering (e.g., K-means) and dimensionality reduction (e.g., PCA) to discover hidden structures or patterns in data without prior labels, often used for exploratory data analysis or feature learning.