Introduction to Regression Models: Training and Evaluation

Predicting numerical values—such as housing prices, market trends, or customer lifetime value—is a fundamental task in data science. Understanding how to build and evaluate regression models is the essential first step toward mastering machine learning. In this course, you will transition from a beginner to confidently training and validating your own predictive algorithms. You will learn how to prepare your data, train various regression models, and critically assess their performance using standard evaluation metrics. What you'll learn: Understand fundamental regression concepts and terminology, including dependent and independent variables. Prepare and clean numerical datasets using modern Python libraries. Train linear and non-linear regression models to identify patterns and make accurate numerical predictions. Evaluate model performance using key metrics such as Mean Squared Error and R-squared. Apply cross-validation techniques to ensure your models generalize well to unseen data. Interpret model coefficients and feature importance to explain your predictions. You will start by exploring core conceptual foundations and key terminology before diving into hands-on data preparation. From there, you will progress through training basic models, evaluating their accuracy, and applying validation techniques to optimize performance. This course is designed for aspiring data analysts, software developers, and beginners who want to start their journey in machine learning. No prior experience with predictive modeling is required, and only a basic familiarity with Python is recommended. Start reading today to master the core principles of regression and begin building your own predictive models.