Once you have trained your model, how do you know whether it will generalize well to new data? In this course, you will focus on techniques that can be used to properly evaluate and improve a model's performance with the view toward producing the best model for your data and machine learning problem. You will explore different model selection methods that are used to find the best-performing model, and you will apply common out-of-sample validation methods that are used to test your model on unseen data in support of model selection.

You will also discover how both hyperparameter configurations as well as feature combinations play roles in model performance. Using your own implementation along with built-in scikit-learn libraries, you will determine the optimal hyperparameter configuration for your model and perform feature selection techniques to find the combination of features that results in the best model performance.

You are required to have completed the following courses or have equivalent experience before taking this course:

• Machine Learning Foundations
• Managing Data in Machine Learning
• Training Common Machine Learning Models
• Training Linear Models