Learn how to apply and evaluate linear regression models in Python through a structured, hands-on introduction to supervised machine learning. This course guides you through the complete regression workflow, from identifying a machine learning use case and preparing your environment to analyzing data, building a model, and evaluating prediction accuracy.

Designed for beginners and aspiring data professionals, the course introduces the essential Python libraries for regression, exploratory data analysis (EDA), and graphical techniques for understanding data distributions, variable relationships, and outliers. You will then construct a simple linear regression model, generate predictions, and evaluate model performance using standard metrics and prediction comparisons to determine how well the model fits real-world data. What makes this course unique is its project-driven learning approach that combines practical demonstrations, clear conceptual explanations, and structured assessments. Practice and graded quizzes aligned with Bloom's Taxonomy reinforce key concepts and help you build confidence as you progress. By the end of the course, you will be able to prepare data for regression, analyze relationships between variables, build and evaluate a linear regression model in Python, and interpret results to validate predictive performance. If you want to develop a strong foundation in Python-based supervised learning and regression analysis, this course provides a practical path to achieving that goal.