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 course is part of the Sports Performance Analytics Specialization
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Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Course 5 of 5 in the
Intermediate Level
Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.
Approx. 12 hours to complete
English
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Course 5 of 5 in the
Intermediate Level
Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.
Approx. 12 hours to complete
English
Offered by
Syllabus - What you will learn from this course
3 hours to complete
Machine Learning Concepts
3 hours to complete
7 videos (Total 75 min), 3 readings, 1 quiz
4 hours to complete
Support Vector Machines
4 hours to complete
4 videos (Total 51 min), 2 readings, 1 quiz
3 hours to complete
Decision Trees
3 hours to complete
4 videos (Total 58 min), 2 readings, 1 quiz
3 hours to complete
Ensembles & Beyond
3 hours to complete
5 videos (Total 102 min), 3 readings, 1 quiz
Reviews
- 5 stars69.23%
- 4 stars23.07%
- 2 stars7.69%
TOP REVIEWS FROM INTRODUCTION TO MACHINE LEARNING IN SPORTS ANALYTICS
by LROct 24, 2022
Very hands-on course, I could understand all techniques available to model sports.
by NMDec 4, 2022
Outstanding course! Really interesting and tutor was really enthusiastic which kept the videos and assessments easy to work through.
About the Sports Performance Analytics Specialization

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