Chapter 1: What is a Model?
In this introduction, we’ll discuss what models are along with a few examples of real-life models.
Chapter 2: Common guiding principles
In this chapter, we’ll discuss some common guiding principles when building models, regardless of the task.
Chapter 3: Evaluation techniques
In this chapter, we’ll discuss evaluation techniques such as k-fold cross-validation, holdout, etc.
Chapter 4: Evaluating regression
In this chapter, we’ll discuss common metrics used to evaluate regression models.
Chapter 5: Evaluating classification
In this chapter, we’ll discuss common metrics used to evaluate classification models.
Chapter 6: Evaluating clustering
In this chapter, we’ll discuss common metrics used to evaluate clustering models.
Chapter 7: Hyperparameter tuning
In this chapter you’ll learn about the concept of hyperparameter tuning, including grid search and random search.