An Introduction to Linear Regression Analysis, The Very Basics - Coding Least Square Error | NerdML
Tutorial introducing the idea of Linear Regression Analysis and the least square method typically used in a statistics class. 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. I have tried to cover all aspects including inner functionality of this algorithm, mathematics derivations used behind Simple Linear Regression.
What we have studied in last session we have coded in this session with & without using Scikit-learn library.
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Please find the previous Video link - An Introduction to Linear Regression Analysis, The Very Basics - Mathematical Explanation | NerdML : • An Introduction to Linear Regression Analy...
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Prerequisites
Basic understanding of Linear Algebra, Probability, Matrix & Python programming including pandas, numpy, scikit learn & some visualization tools.
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Creator : Rahul Saini
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