Artificial Intelligence #1: Linear & MultiLinear Regression

Regression techniques for students and professionals. Learn Linear & Multilinear Regression and code them in python

In statistics, Linear Regression is a linear approach for modeling the relationship between a scalar dependent variable Y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression.

In Linear Regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models.

In this Course you learn Linear Regression & Multilinear Regression
You learn how to estimate and predict simple and single variable regression to find the possible future output Next you go further
You will learn how to estimate output of Multivariable model by using Multilinear Regression

In the first section you learn how to use python to estimate output of your system. In this section you can estimate output of:

Random Number
Diabetes
Boston House Price
Built in Dataset
In the Second section you learn how to use python to estimate output of your system with multivariable inputs.In this section you can estimate output of:

Global Temprature
Total Sales of Advertising Campaign
Built in Dataset

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