Use ensemble algorithms to combine many individual predictors to produce better predictions. Apply advanced techniques such as dimensionality reduction to combine features and build better models. Evaluate models and choose the optimal hyper-parameters using cross-validation. Use ensemble algorithms to combine many individual predictors to produce better predictions. Apply advanced techniques such as dimensionality reduction to combine features and build better models.

Ensemble methods offer a powerful way to improve prediction accuracy by combining in a clever way predictions from many individual predictors. In this course, you will learn how to use ensemble methods to improve accuracy in classification and regression problems. When using Predictive Analytics to solve actual problems, besides models and algorithms there are many other practical considerations that must be considered like which features should I use, how many features are enough, should I create new features, how to combine features to give the same underlying information, which hyper-parameters should I use? We explore topics that will help you answer such questions. Artificial Neural Networks are models loosely based on how neural networks work in a living being.