In this new online course, you will explore supervised machine learning techniques using the Python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes. This course is part of the 5-course series “Sports Performance Analytics.” You will examine data from professional sports leagues such as the NHL and MLB as well as wearable devices such as the Apple Watch and inertial measurement units (IMUs). By the end of the course, you will have a broad understanding of how classification and regression techniques can be used to enable sports analytics across athletic activities and events.
This course is free for U-M alumni, students, faculty, and staff.
