Linear regression is one of the easiest to implement machine learning algorithms. Learn how to use Numpy to implement a basic linear regression model from the ground up

These days, it’s easy to fit pretty much any model you can think of with one library or another, but how much do you really learn by calling .fit() and .predict()? While it’s certainly much more practical to use a framework like python’s statsmodels or scikit-learn for the normal use-case, it seems equally logical that when learning data science it makes a lot of sense to get a feel for how these models actually work. Below we show how to use numpy to implement a basic linear regression model from the ground up. Let’s get started!

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Applied Data Analysis in Python Machine learning and Data science, we will investigate the use of scikit-learn for machine learning to discover things about whatever data may come across your desk.

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