Introduction to Machine Learning in Sports Analytics

In this course students 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. Building on the previous courses in the specialization, students will apply methods such as support vector machines (SVM), decision trees, random forest, linear and logistic regression, and ensembles of learners to 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 students will have a broad understanding of how classification and regression techniques can be used to enable sports analytics across athletic activities and events

• Understand classification and regression techniques in sports analytics
• Apply supervised learning with scikit-learn
• Analyze real sports data to comprehend algorithms
• Predict athletic outcomes using machine learning models
• Use methods like SVM, decision trees, and random forest

• Basic knowledge of Python
• Recommended to complete introductory Python courses