Day 19: Boosting & Gradient Boosting Explained | Machine Learning | Learn ML Everyday

Day 19 – Learn ML Everyday 🚀 In this video, we explore Boosting and Gradient Boosting, two important concepts in Ensemble Learning. Unlike Bagging, where models are trained independently, Boosting builds models sequentially, where each new model focuses on improving the errors made by previous models. 📌 Topics Covered What is Boosting? Why do we need Boosting? How Boosting works Sequential model training Weak Learners Combining multiple weak learners Bagging vs Boosting What is Gradient Boosting? How Gradient Boosting works Understanding residuals/errors Gradient Boosting step-by-step Advantages of Gradient Boosting Limitations of Gradient Boosting Real-world applications 🔥 Simple Understanding Bagging: Train multiple models independently → Combine their predictions Boosting: Train models sequentially → Each model tries to improve the previous model Gradient Boosting: Builds models step-by-step to minimize prediction errors. This video is part of my Machine Learning Zero to Hero – Learn ML Everyday series. 📚 Day 19 completed! ✅ #MachineLearning #Boosting #GradientBoosting #EnsembleLearning #Python #DataScience #ML #LearnMLEveryday #AI #MachineLearningTutorial