# Linear Regression

# Chapter 1: Introduction

In this introduction, we’ll provide an intuition about how Linear Regression works.

# Chapter 2: Linear Regression

In this chapter, we’ll show how to implement Linear Regression from scratch.

# Chapter 3: Gradient Descent for Linear Regression

In this chapter, we’ll give a gentle introduction to gradient descent and why it may be better suited than analytical optimization.

# Chapter 4: LASSO regularization

In this chapter, we’ll introduce LASSO regularization as a way to prevent overfitting.

# Chapter 5: Ridge regularization

In this chapter, we’ll introduce Ridge regularization as a way to prevent overfitting.

# Chapter 6: ElasticNet regularization

In this chapter, we’ll introduce ElasticNet regularization as a way to prevent overfitting.

# Chapter 7: Summary

In this chapter, we’ll summarize what was taught in the previous chapters and provide links to useful resources.