Linear Regression Model

Linear regression Model is a supervised learning algorithm in which we try to predict a linear equation with one or more than one independent variables. Given the independent variables, the equation is able to predict the dependent variable. The independent variables are known as features. The coefficients of the independent variables are known as parameters. The predicted equation is known as hypothesis. Following are the notes from the ML course by Stanford Professor of AI Andrew NG.

Introduction