A few weeks ago, I revisited my old AI/ML projects. As I looked through the code, I felt something was missing. I was using models like RandomForestRegressor and XGBRegressor , getting decent results… but I didn’t feel I truly understood what was happening under the hood. So I made a decision: Instead of consuming more tutorials, I would build my own comprehensive Hands-on AI Tutorial — first for myself, and then for the community. Today, I’m happy to announce that Chapter 1: Regression is complete ! 🎉 What’s Inside Chapter 1 I implemented and compared 5 different regression techniques on real-world datasets: Linear Regression — Implemented from scratch using the Normal Equation (NumPy only) Decision Tree Regression Random Forest Regression XGBoost Regression — This one consistently delivered impressive performance Support Vector Regression (SVR) with linear, RBF, and polynomial kernels For every algorithm, I did the following: Built a from-scratch version (where applicable) Compared it with the industry…