Full course description
Course Overview
This course introduces classification as a core machine learning task, with a focus on logistic regression as one of the most widely used classification methods. You will learn how classification differs from prediction and how models can be used to assign observations to specific categories.
Building on these foundations, the course explores the key concepts behind logistic regression, including how models are fit, how outputs are interpreted, and how results can be used to support decision-making. You will also gain hands-on experience implementing logistic regression in Python and working with real-world data to better understand model behavior and performance.
Why enroll in this course?
Gain essential skills in one of the most important areas of machine learning—classification. This course provides a practical introduction to logistic regression, helping you understand not just how to build models, but how to interpret and communicate their results effectively.
Whether you are a business professional, analyst, or aspiring data scientist, this course will equip you with the tools to make data-driven decisions and confidently apply classification techniques in real-world scenarios.
What will you learn?
By the end of this course, you will be able to:
- Explain classification as a machine learning task and distinguish it from prediction
- Understand the key concepts and assumptions behind logistic regression
- Interpret logistic regression coefficients for both continuous and categorical variables
- Build and implement logistic regression models in Python
- Evaluate model outputs and use them to inform decisions
Structure
The course is completely self paced. It will take you approximately 20 hours to complete all four modules. Activities include, video lessons, readings, and self reflection activities. Upon successful completion of this course, you will receive a certificate of completion.
Smith Executive Education Homepage | Contact us: rhsmith-execed@umd.edu