In this module, we talk about tuning a machine learning model.

Most if not all machine learning models come with some "hyperparameters" -- metadata of the model that cannot be learned and needs to be optimized by the user. The process of optimizing these hyperparameters is called model tuning or finetuning. This module introduces concepts including hyperparameters, regularization, cross validation, grid search, and how to apply them in scikit-learn. We also discuss tuning the XGBoost model in AWS.

Please go through the provided materials to study for the module.