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 to minimize the error between predicted and actual labels. Unsupervised learning, on the other hand, employs techniques such as clustering (e.g., K-means, hierarchical clustering) and dimensionality reduction (e.g., PCA) to explore data structure and relationships without explicit labels, often used for exploratory data analysis and pattern recognition.