Linear Regression Machine Learning (python example in jupyter notebook) || Intro to Machine Learnin

In This video you will learn what is Linear Regression and what are different methods that we can use in Linear Regression.
How to implement Linear Regression Practically in Jupyter Notebook.

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When you start looking into linear regression, things can get very confusing.

The reason is because linear regression has been around for so long (more than 200 years). It has been studied from every possible angle and often each angle has a new and different name.

Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).

When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, literature from statistics often refers to the method as multiple linear regression.

Different techniques can be used to prepare or train the linear regression equation from data, the most common of which is called Ordinary Least Squares. It is common to therefore refer to a model prepared this way as Ordinary Least Squares Linear Regression or just Least Squares Regression.

Description Reference:
machinelearningmastery.com


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