Introduction to Machine Learning in Sports Analytics

In this course, students will explore supervised machine learning techniques using the scikit learn (sklearn) python toolkit and real-world sports data to understand both machine learning algorithms and how to predict sports performance. Building on previous courses in the specialization, students will apply techniques such as support vector machines (SVM), decision trees, random forests, linear and logistic regression, and learner ensembles to learn 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, students will have a broad understanding of how classification and regression techniques can be used for sports analytics across all sports and competitions.