Gradient Boosting Machine (GBM) – Complete Math & Statistics Tutorial

In this video, we explain the Gradient Boosting Machine (GBM) algorithm from scratch with complete mathematical and statistical foundations. This tutorial covers how Gradient Boosting works internally, including: ✔ Boosting vs Bagging explained clearly ✔ Additive model and stage-wise learning ✔ Loss functions (MSE, Log Loss) with math ✔ Gradient descent intuition and derivations ✔ Pseudo-residuals explained step by step ✔ Gradient Boosting Classifier vs Regressor ✔ Bias–Variance tradeoff in boosting ✔ Overfitting control using learning rate, depth, and trees 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 📬 CONNECT WITH ME: LinkedIn: www.linkedin.com/in/vivekpol Twitter: https://x.com/polvivek77 GitHub: https://github.com/polvivek77/Movie-R... Dataset Link: https://www.kaggle.com/datasets/tmdb/... 🔔 Subscribe and hit the bell icon to get new project videos every week. Business enquiries: [email protected] #GradientBoosting #MachineLearning #DataScience #MLAlgorithms #ArtificialIntelligence