This chapter explores supervised learning techniques where algorithms learn from labeled training data to make predictions. It begins with logistic regression for binary classification problems, using the sigmoid function to output probabilities between 0 and 1. Softmax regression extends this to multi-class problems. The chapter covers k-nearest neighbors (kNN), which classifies data points based on their similarity to training examples. Decision trees use entropy and information gain to create interpretable classification rules, while random forests combine multiple decision trees to reduce overfitting through ensemble methods. Naive Bayes applies Bayes’ theorem with independence assumptions for probabilistic classification, particularly effective for text classification. Finally, support vector machines (SVM) find optimal decision boundaries by maximizing margins between classes. Each technique is demonstrated through hands-on Python examples using real datasets, showing practical applications in various domains from healthcare to finance.