This is essentially the "Hello World" tutorial for machine learning. Linear regression is used to understand the relationship between input (x) and output (y) variables. When there is only one input variable (x), it's called simple linear regression. You've probably seen this technique used in simple statistics.

The most common technique used to train a linear regression equation is called Ordinary Least Squares. So, when we use this process to train a model in machine learning it's usually referred to as Ordinary Least Squares Linear Regression.

A simple regression model for input (x) and output (y) can be modeled as such:

y = B0 + B1*x

The coefficient B1 (beta) is an estimate of the regression slope, and the additional coefficient B0 estimates the regression intercept giving the line an additional degree of freedom.