In measurements, Linear Regression is a direct approach for demonstrating the connection between a scalar ward variable Y and at least one logical factors (or free factors) indicated X. The instance of one informative variable is called straightforward direct relapse. For more than one informative variable, the procedure is called different direct relapse.
In Linear Regression, the connections are demonstrated utilizing straight indicator works whose obscure model parameters are assessed from the data. Such models are called straight models.
In this Course you learn Linear Regression and Multilinear Regression
You learn how to evaluate and foresee straightforward and single variable relapse to locate the conceivable future yield Next you go further
You will learn how to assess yield of Multivariable model by utilizing Multilinear Regression
Udemy course :https://www.udemy.com/artificial-intelligence-1-linear-multilinear-regression/
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