Python Machine Learning | Linear Regression Ordinary Least Square OLS method in Statsmodels package

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In this video, part of my series on "Machine Learning", I explain how to perform Linear Regression for a 2D dataset using the Ordinary Least Squares method.

In this python machine learning video I have talked about how you can achieve the ordinary least square linear regression using python statmodels package.

We review what the main goals of regression models are, see how the linear regression models tie to the concept of linear equations, and learn to interpret the coefficients of a simple linear regression model with an example.

This is the first video in what will be, or is (depending on when you are watching this) a multipart video series about Simple Linear Regression. In the next few minutes we will cover the basics of Simple Linear Regression starting at square one. And for the record, from now on if I say "regression" I am referring to simple linear regression as opposed to multiple regression or models that are not linear.

Regression allows us to model, mathematically, the relationship between two or more variables. For now, we will be working with just two variables; an independent variable and a dependent variable. The truth is, when we talk about how "good" a regression model is we are actually comparing it to another specific model. Oftentimes, students do not realize this.

So in this video, we are going to talk about that idea. I will also begin introducing basic terminology and concepts that will carry you through your work using regression. There are no formulas or calculations in this video. We are just introducing the underlying meaning behind "good" regression models.

So if you are new to Regression or are still trying to figure out exactly what it even IS...this video is for you.