Module
Linear Regression and Least Squares
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About
Model, squared loss, normal equations, geometric view, gradient descent, and simple feature engineering.
Goal
Solve a multiple linear regression problem both in closed form and via GD; interpret coefficients and residuals; compute R² / MSE on held-out data.
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