Some of the most common algorithms are regressions. A regression predicts a number from infinitely possible number outputs. Linear regressions fit a straight line to a dataset, while non-linear regressions fit a curved line to a dataset. Using only one input variable is called univariate linear regression, but models can have much more than just one. A practical example of linear regression is predicting home prices based on their square footage.

Defining the Model

The Cost Function

Gradient Descent for Linear Regression

Multiple Feature Linear Regression

Gradient Descent for Multiple Linear Regression

Regularized Linear Regression