Ensemble Learning is a machine learning technique that combines multiple models to improve overall performance. Instead of relying on a single model, it aggregates the predictions from various models, which can lead to more accurate and robust results. This approach helps to reduce errors and increase reliability by leveraging the strengths of different algorithms. There are several methods of ensemble learning, including Bagging and Boosting. Bagging involves training multiple models independently and averaging their predictions, while Boosting focuses on sequentially training models, where each new model corrects the errors of the previous ones. Together, these methods enhance predictive power and reduce overfitting.