Today we will look at a simple linear regression model to grasp all of the math going on behind the scenes. We must consider the math. I’ll be honest; machine learning mathematics is difficult.

*Note, this article focuses more behind the mathematics of linear regression. You will need to understand the basics of partial derivatives and some linear algebra. If you are not too comfortable with these topics, bookmark this article, watch some Khan Academy videos and then have a read. You’ll thank me later.*

Have you ever felt like you never properly understood gradient descent, regression or loss functions? Did you brush these concepts aside and focus all on the coding? Yeah, me too.

To understand what’s going on behind the algorithms, we must consider the math. I’ll be honest; machine learning mathematics is difficult. However, once we break down each concept and take a step-by-step approach, it will feel like a new world of understanding just emerged!

Today we will look at a simple linear regression model to grasp all of the math going on behind the scenes. Let’s get started, shall we?

Regression is a form of predictive analysis that examines the relationship between one or more independent variables to a dependent variable. It’s basically like functions: some value of x is inputted, that value is manipulated through certain coefficients like a slope or intercept, and finally another value is outputted.

**Linear regression is a technique that is used when the shape of the dataset best resembles a straight line.**

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