The motivation behind Linear Regression is probably one of the most intuitive. We could show a chart with a bunch of points to a child and his brain will automatically trace a line. When I explain Linear Regression to non-math people they understand it – it just makes sense.
How can we turn this intuition into a mathematical process? How can we use it to create models and deliver insights from real world data?
In this series “The Math of Linear Regression“, I will derive all the equations needed from start to finish, and explain them to you step by step. I will also show you how to implement Linear Regression in Python while making a direct link with theoretical concepts.
We will also explore the machine learning implementation of Linear Regression. What I have noticed with online courses is that they don’t explain how to actually tune the machine learning model. This is the most important part, and will go through it as well.
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