Five Easy Pieces of Linear Regression

The post demonstrates five mathematically distinct algorithms—Normal Equation, Gradient Descent, SVD pseudo-inverse, scipy.optimize, and a fifth method—for solving linear regression, all yielding identical results. It argues that deeply understanding linear regression is the most valuable foundation for statistics, ML, and AI, as its intuitions transfer directly to neural networks, Gaussian processes, and Bayesian inference. Each method is implemented from scratch in Python on a synthetic dataset to illustrate the underlying math.