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.
What you'll learn
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.
Table of contents
- Playing the Odds with Logistic Regression 7m
- Working Smart with Logistic Regression 4m
- Applications of Logistic Regression - Analysis, Allocation 5m
- Applications of Logistic Regression - Prediction, Classification 4m
- Logistic Regression and Linear Regression - Similarities 4m
- Logistic Regression and Linear Regression - Differences 5m
- Logistic Regression and Machine Learning 7m