In the previous chapter, we got started with the basics of supervised machine learning. In this chapter, we will dive into the guts of several popular supervised learning algorithms within the parametric modeling family. We'll start this chapter by formally introducing parametric models. Then, we'll introduce two very popular parametric models: linear and logistic regression. We'll spend some time looking at their inner workings and then we'll jump into Python and actually code those workings from scratch.
In this chapter, we will cover the following topics:
- Parametric models
- Implementing linear regression from scratch
- Logistic regression models
- Implementing logistic regression from scratch
- The pros and cons of parametric models
