Supervised learning is a type of machine learning where algorithms learn from labeled datasets to make accurate predictions. It is widely used in classification and regression tasks, such as fraud detection, speech recognition, and medical diagnosis.

How Supervised Learning Works
Supervised learning models are trained using input-output pairs, where the algorithm learns to map inputs to correct outputs. Common algorithms include linear regression for predicting numerical values, decision trees for rule-based classification, and neural networks for complex pattern recognition. The model continuously improves its accuracy by minimizing errors using optimization techniques like gradient descent.

Why Supervised Learning Matters
Supervised learning powers applications such as email spam filtering, stock market forecasting, and self-driving car perception. It improves automation in healthcare, finance, and cybersecurity by providing accurate predictions based on historical data. As AI adoption grows, supervised learning remains fundamental to training intelligent systems for real-world applications.