Supervised Learning: Teaching Machines with Labeled Data

Supervised learning is one of the most commonly used machine learning approaches. In this method, models are trained using labeled datasets, meaning each input data point has a corresponding output label. The algorithm learns patterns from the training data and applies them to new, unseen data.

Some key supervised learning algorithms include:

• Linear Regression: Used for predicting continuous values, such as house prices or stock prices, based on historical data.
• Logistic Regression: Commonly used for binary classification problems, such as spam detection or medical diagnoses.
• Support Vector Machines (SVM): A powerful classification algorithm that finds the best decision boundary between different categories.
• Decision Trees and Random Forests: These algorithms help in both classification and regression tasks by learning decision rules from the data.

Supervised learning is widely used in applications like fraud detection, speech recognition, and predictive analytics.