In this introduction, we’ll discuss what models are along with a few examples of real-life models.

In this chapter, we’ll discuss some common guiding principles when building models, regardless of the task.

In this chapter, we’ll discuss evaluation techniques such as k-fold cross-validation, holdout, etc.

In this chapter, we’ll discuss common metrics used to evaluate regression models.

In this chapter, we’ll discuss common metrics used to evaluate classification models.

In this chapter, we’ll discuss common metrics used to evaluate clustering models.

In this chapter you’ll learn about the concept of hyperparameter tuning, including grid search and random search.