Machine Learning With Python: 31 Hands-On Tutorials

Python Machine Learning

From your first model to a deployed one. These 31 tutorials cover preprocessing, feature engineering, regression, decision trees, random forests, XGBoost, SVMs, clustering, PCA, model evaluation, hyperparameter tuning, and deployment, all with scikit-learn. The series wraps with a full house price prediction project.

Google Colab GPU Setup: Where to Run Deep Learning for Free

Your laptop does not have to suffer through deep learning. A free Google Colab GPU (Graphics Processing Unit) hands you a real Tesla T4 in a browser tab: no CUDA install, no gaming rig, no bill. This post walks through enabling it, proving it is on with one small check, the session rules that catch beginners, and a timed CPU vs GPU race you can run yourself. “The best way to get started is to stop talking and begin doing.” ... Read More

Debugging ML Models in Python: 5 Real Failure Cases

Debugging ML models is a different sport from debugging normal code. Your Python never throws an exception, the pipeline runs green, and yet the model is quietly wrong. This tutorial walks through five real production failures the way a senior reviewer would, each one shown breaking on tested code and then fixed: training loss that diverges, a 99% accuracy that is a lie, a model that shines offline and falls apart online, label leakage caught after deploy, and a loss ... Read More

CPU vs GPU vs TPU vs NPU: AI Hardware Explained Simply

Try to run a real AI model and CPU vs GPU vs TPU stops being trivia: it decides whether the model even loads. The marketing is loud and the plain explanation is rare, so this post is the plain explanation. You will learn why AI leans on massively parallel chips, why “does it fit?” matters before “is it fast?”, and exactly what hardware each stage of this series needs. “People who are really serious about software should make their own ... Read More