Introduction to Generalized Linear Models for Regression and Classification

Linear regression is a powerful tool, but real-world data rarely follows a perfect straight line. To model complex, non-linear relationships and diverse response variables, data analysts and scientists rely on Generalized Linear Models (GLMs). This text-based course guides you through the core concepts and practical implementations of GLMs without overwhelming jargon. By reading through clear explanations and practical examples, you will transition from basic linear models to advanced regression and classification techniques. You will learn how to select the right link functions, handle non-normal error distributions, and transform features to capture complex patterns with high efficiency. What you'll learn: - Understand the foundational theory of GLMs, including link functions and exponential family distributions. - Apply logistic, Poisson, and gamma regression to model binary, count, and continuous data. - Implement feature transformations to capture non-linear patterns without complex machine learning black boxes. - Evaluate model performance using modern diagnostics, deviance, AIC, and cross-validation. - Write clean, modern Python code using statistical libraries and proper type hints to build robust models. You will start with essential statistical definitions and the transition from ordinary least squares to generalized models. From there, you will read through step-by-step code implementations and apply your knowledge through structured written exercises. This course is designed for aspiring data scientists, analysts, and programmers who want to expand their predictive modeling toolkit. No prior experience with GLMs is required, though a basic familiarity with Python and algebra will help you get the most out of the material. Start reading today to master the versatile world of generalized linear models.