This hands-on course empowers learners to apply and evaluate linear regression techniques in Python through a structured, project-driven approach to supervised machine learning. Designed for beginners and aspiring data professionals, the course walks through each step of the regression modeling pipeline—from understanding the use case and importing key libraries to analyzing variable relationships and predicting outcomes.

In Module 1, learners will identify, describe, and prepare the foundational elements of a machine learning project. Through univariate and graphical analysis, they will recognize distribution patterns, outliers, and data characteristics critical to model readiness. In Module 2, learners will analyze variable relationships, construct a regression model, and evaluate its predictive performance using standard metrics and visualizations. By the end of the course, learners will confidently interpret model results and validate them against actual outcomes—equipping them with the core skills to build and assess linear regression models using Python. This course blends practical demonstrations, clear conceptual explanations, and structured assessments—including practice and graded quizzes aligned with Bloom’s Taxonomy—to promote deep, outcome-oriented learning.