Regression Analysis | Regression Coefficients | Machine Learning For Beginners | Great Learning

Regression analysis is a reliable method of identifying which variables have an impact on a topic of interest. The process of performing a regression allows you to confidently determine which factors matter most, which factors can be ignored, and how these factors influence each other. This video on Regression Analysis will help you understand what Regression is and how it works with practical implementation.

The models in regression are very popular in machine learning for predicting target variables on a continuous scale though. Regression models are one of the most widely used models in machine learning. Regression analysis can be treated as a kind of future crystal ball i.e. predictive modeling technique. This analysis looks into the relationship between dependent variables and independent variables i.e predictive variables through independent variables.

In short, we can say this “Regression is a parametric technique which uses a statistical process to draw out the values of predictive variables (Unknown value) with given inputs of independent variables (known value) sets.” Because of its parametric in nature, the output can either astonishing or disastrous. The whole idea is to reduce the distance between a line and data points in order to get the best fit result. Minimum distance will produce minimum errors. in this tutorial, we have covered all the important topics such as, What is Linear regression, How it works, What are its advantages, and much more that will help you to understand this concept with ease.

The following pointers will be covered in this video:
00:00:00 Introduction
00:00:42 Agenda
00:00:50 Case Study
00:32:27 Linear Regression
02:43:35 Demo

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[ZahidHasan-cctf]

Thanks for the amazing lesson. But kindly do something about audio quality. It's difficult to hear it properly even with maximum volume setting.

[statisticsappliedmathemati]

I'm just here to say that assumptions are important since they make the best estimator and in each regression analysis we have to verify those assumptions or results would be misleading