Practical guide to generalized linear models (GLMs) for non-normal data. Learn how to select distributions (binomial, Poisson, Gamma), apply link functions (logit, log), avoid common mistakes, and implement in R/Python. Includes step-by-step workflow, diagnostics, and real-world case examples.