Regression Is Not a Formula. It’s a Derivation. Most Machine Learning content today focuses on usage, which library to import, which function to call, which parameter to tune. Very little time is spent on origins. In this lecture, I go back to first principles and build Linear Regression and Binary Logistic Regression from scratch, starting with Maximum Likelihood Estimation. No shortcuts. No memorized loss functions. No “just trust the intuition”. You’ll see: • How probability becomes optimizat