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

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

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

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

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

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

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