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.
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.
Course Overview My name is Vitthal Srinivasan. Welcome to my course, Understanding and Applying Logistic Regression. I am a cofounder of the startup of the Loonycorn and before this I worked at Google and studied at Stanford. What's the smart way to approach a deadline? Start an hour before the deadline? Good luck with that. A year in advance then? Well, that's probably overkill. The smart way is to start just late enough that you're still sure of making it in time and that is exactly what logistic regression can help you with. Estimating the odds of and how they change as actions do. Some of the major topics that we'll cover in this course include logistic regression and its applications, how logistic regression is linked to linear regression and machine learning, the S-curve and its standard mathematical form, and lastly, how to predict whether a stock will go up or down using three technologies: Excel, R, and Python. By the end of this course you will have a strong applied knowledge of logistic regression that will help you solve complex business problems. You'll know how to build robust regression models in any one of these three powerful tools: Excel, R, and Python. And you'll know how to interpret the S-curve. I hope you'll join me on this journey to learn how to play the odds and to quantify probabilities with this course, Understanding and Applying Logistic Regression here at Pluralsight.