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…