Linear Regression / Gradient Descent from Scratch

Problem Overview

Implement simple linear regression using gradient descent from scratch. Given paired data (X, Y), write code to learn the regression weights by iteratively minimizing the Mean Squared Error (MSE) loss. You must correctly compute MSE — i.e., sum of squared residuals divided…

• 4 candidate-reported follow-ups — the exact probes interviewers asked, with the trigger for each
• The rest of the problem statement — full requirements, constraints, and edge cases
• Approach and trade-offs — what passing candidates did, and the mistakes that sink people