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.