Linear Regression from First Principles: Least Squares, Geometry and Gradient Descent
Fitting a straight line is the oldest trick in machine learning and still the one most worth understanding completely. This article builds linear regression from a five-point example to the normal equations, shows why least squares is a projection and a maximum-likelihood estimate at the same time, trains it with gradient descent you can steer, and then puts it to work on a real dataset to see where it holds up and where it breaks.
