Linear Regression from Scratch: Math + Code (No Black Boxes)

Linear Regression from scratch. No libraries, no black boxes. Just the math derived by hand, then implemented in pure NumPy. In this video: Deriving the cost function (MSE) from first principles Working out the gradient descent update rule step by step Implementing linear regression in NumPy, no sklearn Sanity checking the math against the code This is part of my scratch-to-splendid series, where I implement ML algorithms and deep learning architectures from scratch. The goal is to actually understand what's happening under the hood, not just call .fit(). Code: https://github.com/amithkumars-ds/SCR... Next up: Logistic Regression from scratch. #MachineLearning #LinearRegression #NumPy #GradientDescent #MLFromScratch