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June 24, 2024
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This course is part of Sports Performance Analytics Specialization
Instructor: Christopher Brooks
4,391 already enrolled
Included with
(24 reviews)
Recommended experience
Intermediate level
Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.
(24 reviews)
Recommended experience
Intermediate level
Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.
Gain an understanding of how classification and regression techniques can be used to enable sports analytics across athletic activities and events.
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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.
This week will introduce the concept of machine learning and describe the four major areas of places it can be used in sports analytics. The machine learning pipeline will be discussed, as well as some common issues one runs into when using machine learning for sports analytics.
7 videos3 readings1 assignment1 ungraded lab
In this week students will learn how Support Vector Machines (SVM) work, and will experience these models when looking at both baseball and wearable data. Coming out of the week students will have experience building SVMs with real data and will be able to apply them to problems of their own.
4 videos2 readings1 assignment
This week will focus on interpretable methods for machine learning with a particular focus on decision trees. Students will learn how these models work in general, and see special uses of decision trees in combination with regression methods. In this week students will come to better understand how the python sklearn toolkit can be used for a breadth of supervised learning tasks.
4 videos2 readings1 assignment
In this week of the course students will learn how many different models can be used together through ensembles, including the random forest method as a common use, as well as more general methods available in sklearn such as stacking and bagging. By the end of this week students will have a broad understanding of how methods such as SVMs, decision trees, and logistic regression can be used together to solve a problem with increasing performance.
5 videos3 readings1 assignment
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
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24 reviews
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Reviewed on Oct 24, 2022
Very hands-on course, I could understand all techniques available to model sports.
Reviewed on Oct 30, 2024
Provide solid foundation for beginning supervised ML
Reviewed on May 6, 2023
Well-structured notebook, resourceful, applicable to real-world projects, clear and entertaining teaching. Highly satisfied. One of the best modules in the entire specialization.
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