Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This course will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking. The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You will then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don’t miss out on key topics, such as like resampling methods. As you progress, you will get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The course will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding lessons, you will delve into advanced ensemble models using neural networks, natural language processing, and more. You will also be able to implement models such as fraud detection, text categorization, and sentiment analysis. By the end of this course, you’ll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.