Full course description
Course Overview
This course introduces the fundamentals of machine learning in a predictive context, with a focus on building and applying linear regression models. You will explore key concepts such as the machine learning process, model evaluation, and the distinction between explanatory and predictive modeling.
As the course progresses, you will examine regularization techniques, including ridge and LASSO regression, to improve model performance and prevent overfitting. In the final module, you will apply these concepts to a real-world dataset using Python, gaining hands-on experience building and evaluating predictive models. This applied component is designed to help you develop practical skills that can be used in personal projects and professional portfolios.
Why enroll in this course?
Develop the practical skills needed to build and evaluate machine learning models with confidence. This course goes beyond theory by emphasizing hands-on application, helping you understand not just how models work, but how to use them effectively in real-world scenarios.
Whether you are an aspiring data scientist, analyst, or technical professional, this course will strengthen your ability to apply predictive modeling techniques and enhance your project portfolio with in-demand machine learning skills.
What will you learn?
By the end of this course, you will be able to:
- Describe the fundamentals of machine learning in a predictive setting
- Differentiate between explanatory and predictive modeling approaches
- Evaluate model performance using common predictive accuracy measures
- Apply regularization techniques, including ridge and LASSO regression
- Build and implement linear regression models in Python using real-world data
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