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Understanding and Applying Logistic Regression

Logistic Regression is a great tool for two common applications: binary classification, and attributing cause-effect relationships where the response is a categorical variable. While the first links logistic regression to other classification algorithms (such as Naive Bayes), the second is a natural extension of Linear Regression. In this course, Understanding and Applying Logistic Regression, you'll get a better understanding of logistic regression and how to apply it. First, you'll discover applications of logistic regression and how logistic regression is linked to linear regression and machine learning. Next, you'll explore the s-curve and its standard mathematical form. Finally, you'll learn whether Google's stock returns will go up or down, using Excel (solver), R, and Python. By the end of this course, you'll have a strong applied knowledge of logistic regression that will help you solve complex business problems.

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Length 2.4 hours
Starts On Demand (Start anytime)
Cost $29/month (Access to entire library- free trial available)
From Pluralsight
Instructor Vitthal Srinivasan
Download Videos On Windows, MacOS, iOS, and Android Pluralsight app
Language English
Subjects Data Science Programming IT & Networking
Tags Data Science Data Analysis Machine Learning Data Professional

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Rating Not enough ratings
Length 2.4 hours
Starts On Demand (Start anytime)
Cost $29/month (Access to entire library- free trial available)
From Pluralsight
Instructor Vitthal Srinivasan
Download Videos On Windows, MacOS, iOS, and Android Pluralsight app
Language English
Subjects Data Science Programming IT & Networking
Tags Data Science Data Analysis Machine Learning Data Professional

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