Building Predictors with Linear and Polynomial Regression in Python

Every data-driven decision relies on predicting the future based on past trends. Understanding how to build predictive models is the essential first step into the world of machine learning and data science. This text-only course guides you through the foundational concepts of regression analysis, helping you transition from understanding basic mathematical relationships to writing clean Python code that fits, evaluates, and optimizes predictive models. What you'll learn: - Understand core regression terminology, from dependent variables to loss functions. - Prepare and clean numerical datasets using modern Python data libraries. - Build and train linear regression models using scikit-learn. - Apply polynomial features to capture non-linear relationships in complex data. - Evaluate model performance using key metrics like Mean Squared Error and R-squared. - Implement modern coding practices including type hints and proper validation split techniques. You will start with the fundamental theory of linear relationships before moving step-by-step through data preparation, model training, and polynomial techniques. Each concept is reinforced with clear written explanations, structured code walkthroughs, and practical exercises to test your understanding. Designed specifically for beginners, this course requires no prior machine learning experience, though basic familiarity with Python is helpful. Start reading today and build your first predictive machine learning model.