This post is based on the linear regression live coding session from Siraj Raval (Udacity).
What is Linear Regression?
Linear regression is a widely used method to find a predictor variable which describes an outcome variable. In simpler words, it describes the relationship of two value sets. In this example, Siraj shows how to do Linear Regression using gradient descent.
Gradient descent is an iterative optimization algorithm widely used in machine learning. In simpler words, we use it to make our linear regression model as precise as possible.
You can find the Github repository here.
I often learn awesome stuff while reading and it makes me want to share that shit. That's what this site is for, hope you not just learn from it but enjoy it like I do!
Also published on Medium.