Simple Linear Regression in Python

Step-by-step follow along | Data Series | Episode 4.3

From the previous two episodes you should now know the underlying theory of Linear Regression, its purpose and how we use gradient descent in optimising our parameters. You can read them here: Episode 4.1, Episode 4.2 .

You can view the code used in this Episode here: SampleCode

Setting up your programming environment

All programming in this series will take place on a program called Anaconda which is the most popular data science toolkit for performing machine learning tasks in Python and R.

You can download Anaconda completely free by visiting https://www.anaconda.com/products/individual

After downloading Anaconda, launch Jupyter Notebook. All our projects will take place on Jupyter Notebook which is a program that enables us to run code on our browser quickly and efficiently.