A few weeks ago I published Chapter 1 of my hands-on AI tutorial series, focused on Regression. Today, I'm excited to share that Chapter 2: Classification is complete.

This series isn't just another collection of notebook tutorials. I'm building it to truly understand how these algorithms work under the hood — implementing them from scratch where it makes sense, comparing them properly, and focusing on concepts that actually matter in interviews and real projects.

What’s in Chapter 2

I implemented and analyzed five core classification algorithms:

Logistic Regression (implemented from scratch with NumPy, plus scikit-learn version)

K-Nearest Neighbors (KNN) Classifier

Random Forest Classifier

XGBoost Classifier

Support Vector Classifier (SVC) with different kernels

Key Focus Areas

This chapter goes deeper than just training models.…