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.…