XGBoost Algorithm Explained Step by Step | ML with Math & Statistics

In this video, we explain the Xtreme Gradient Boosting (XGBoost) algorithm from scratch with complete mathematical and statistical foundations. This tutorial deeply explains how XGBoost works internally, including: ✔ Boosting concept and additive tree models ✔ Objective (loss) function explained mathematically ✔ First-order & second-order Taylor expansion ✔ Gradient and Hessian calculations ✔ Regularization (L1 & L2) in XGBoost ✔ Tree splitting formula and gain calculation ✔ XGBoost Classifier vs Regressor ✔ Why XGBoost is faster and more accurate than GBM ✔ Bias–Variance tradeoff explained clearly This video is ideal for: - Machine Learning beginners - Data Science students - Python developers entering ML - Interview preparation (ML / AI / Data Science roles) This tutorial is part of the playlist: 🎯 Machine Learning Algorithms – From Scratch with Math Subscribe for more Machine Learning, AI, Python & Data Science tutorials. 📬 CONNECT WITH ME: LinkedIn: www.linkedin.com/in/vivekpol Twitter: https://x.com/polvivek77 🔔 Subscribe and hit the bell icon to get new project videos every week. Business enquiries: [email protected] #XGBoost #MachineLearning #DataScience #MLAlgorithms #ArtificialIntelligence