Articles
Long-form, code-first writing on machine learning — from gradient descent from scratch to shipping RAG systems in production.
From Data to Predictions: Understanding Linear Regression Through Statistics and Python
Learn how mean, variance, and covariance become the slope and intercept of a Linear Regression model. Follow one running employee-salary example from raw numbers to predictions, with runnable NumPy, Pandas, Matplotlib and Scikit-learn code — all in your browser.
Linear Regression for Machine Learning: A Complete Beginner's Guide
Learn linear regression from first principles — business intuition, math in plain English, assumptions, gradient descent, Scikit-learn implementation on the Medical Insurance dataset, evaluation metrics, interview questions and a full end-to-end project. Runnable in your browser.
Python for Machine Learning: A Beginner's 7-Chapter Crash Course
Master the seven Python building blocks every ML engineer uses every day — lists, dictionaries, tuples, strings, comprehensions, enumerate/zip, and map/filter/reduce. Runnable in your browser, taught with real-world banking, healthcare and retail examples.
Ordinary Least Squares (OLS) Regression: The Complete Guide
A deep, business-driven tour of OLS regression — mathematical foundations, assumptions, diagnostics, evaluation metrics and a full Python case study on the Medical Insurance dataset. Written for beginners, analysts and interview candidates.
Gradient Descent from Scratch in Python
Implement batch, stochastic and mini-batch gradient descent from first principles — plus momentum, learning-rate decay and how to read a broken loss curve.
A Practical Guide to XGBoost Hyperparameter Tuning
The parameters that actually move the needle, ordered by impact, with a repeatable tuning workflow and a reference configuration.