In this chapter, students will delve into the fundamentals of supervised learning, where the machine learning model is trained on labeled data to make predictions or decisions. They will explore the two main types of supervised learning: classification and regression, and learn about algorithms such as linear regression, logistic regression, decision trees, and support vector machines. Students will also gain an understanding of model evaluation metrics, feature engineering, and the importance of data preprocessing in supervised learning.