Building Linear Regression From Scratch in Python Using NumPy and Matrix Math

A DEV Community article explains how to implement linear regression without scikit-learn, using only NumPy to expose the underlying mechanics. It defines core terms such as features, targets, weights, and the cost function, and shows how a learned hypothesis turns inputs into predictions. The author describes choosing weights by minimizing mean squared error across the dataset, with the squared differences keeping positive and negative errors from canceling. A code example shows how to add a column of ones to the feature matrix so the intercept is computed alongside the other weights. The article then introduces gradient descent as the iterative method for finding weights that minimize error, using a mountain-descent analogy.