# Module overview

## What you will learn

<!-- Give in plain English what the module is about -->

In this module, will go further into details regarding models that use
linear parametrization.
We will see how to use this family of models for both classification and
regression problems. Besides, we will explain how to fight over-fitting using
regularization.
Finally, we will show how linear models can be used with
data presenting non-linearity.

## Before getting started

<!-- Give the required skills for the module -->

The required technical skills to carry on this module are:

- skills acquired during the "The Predictive Modeling Pipeline" module with
  basic usage of scikit-learn;
- skills acquired during the "Selecting The Best Model" module, mainly around
  the concept of underfit/overfit and the usage of cross-validation in
  scikit-learn.

<!-- Point to resources to learning these skills -->

## Objectives and time schedule

<!-- Give the learning objectives -->

In this module, your objectives are to:

- understand the linear models parametrization;
- understand the implication of linear models in both
  regression and classification;
- get intuitions of linear models applied in higher dimensional dataset;
- understand the effect of regularization and how to set it;
- understand how linear models can be used even with data showing non-linear
  relationship with the target to be predicted.

<!-- Give the investment in time -->

The estimated time to go through this module is about 6 hours.