Linear regression with gradient descent

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.

I won’t get into details about the code, if you are interested please watch the videos 1聽or聽2.

You can find the Github repository here.

 

Karl Niebuhr

Karl Niebuhr

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Karl Niebuhr

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