Discover the power of ensemble learning, a technique that combines multiple machine learning models to optimize performance and accuracy. Explore the different types of ensemble learning, such as bagging, boosting, and stacking, each with its own unique features and benefits. Dive deep into bagging techniques, including random forest and bootstrap aggregating, and understand how they improve predictions. Uncover the secrets of boosting techniques like AdaBoost, Gradient Boosting, and XGBoost, and see how they handle complex problems. Finally, explore stacking ensemble learning, a method that combines predictions from multiple models for even better accuracy.