Machine Learning

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Supervised learning learns from examples paired with target labels, such as emails marked spam or not spam. Unsupervised learning looks for structure in data without provided target labels, such as grouping similar customers. Reinforcement learning learns a policy by taking actions and receiving rewards or penalties over time. The key distinction is the learning signal, not whether an algorithm is “intelligent.”

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Overfitting occurs when a model fits details or noise in its training data so closely that it performs poorly on new examples. A common sign is strong training performance but weaker validation performance. You can reduce the risk with representative data, appropriate model complexity, regularization, early stopping, and careful validation. The goal is generalization, not simply minimizing training error.