Supervised learning trains on labeled data to predict outputs, enabling tasks like spam detection, house-price prediction, and cat image recognition with labeled photos. Unsupervised learning finds structure without labels, enabling clustering of news articles by topic and customer segmentation into similar behavior groups. Reinforcement learning trains an agent by interacting with an environment to maximize rewards, powering autonomous decisions in self-driving cars and game AI. Neural networks, especially deep neural networks, consist of multiple layers of interconnected nodes that progressively extract higher-level features for image, speech, and language tasks. Overfitting occurs when a model memorizes training data and fails to