Understanding and Applying Logistic Regression

This course will teach you both the theory and implementation of logistic regression, in Excel (using solver), Python, and R.
Course info
Rating
(25)
Level
Intermediate
Updated
Mar 31, 2017
Duration
2h 22m
Table of contents
Description
Course info
Rating
(25)
Level
Intermediate
Updated
Mar 31, 2017
Duration
2h 22m
Description

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.

About the author
About the author

An engineer and tinkerer, Vitthal has worked at Google, Credit Suisse, and Flipkart and studied at Stanford and INSEAD. He has worn many hats, each of which has involved writing code and building models. He is passionately devoted to his hobby of laughing at his own jokes.

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