Introduction to Machine Learning in Sports Analytics
Learn to apply machine learning techniques using Python and scikit-learn for sports performance analysis.
Learn to apply machine learning techniques using Python and scikit-learn for sports performance analysis.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Sports Performance Analytics Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Build machine learning models for sports performance analysis
Implement SVM and decision trees using scikit-learn
Analyze professional sports and wearable device data
Develop ensemble learning models for improved predictions
Apply cross-validation and model evaluation techniques
Skills you'll gain
This course includes:
4.7 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores supervised machine learning techniques in sports analytics using Python's scikit-learn toolkit. Students learn to apply methods like Support Vector Machines, decision trees, random forests, and ensemble learning to analyze professional sports data and wearable device information. The curriculum covers both theoretical concepts and practical applications, focusing on real-world athletic data from major sports leagues and wearable devices to predict athletic outcomes and analyze performance metrics.
Machine Learning Concepts
Module 1 · 2 Hours to complete
Support Vector Machines
Module 2 · 4 Hours to complete
Decision Trees
Module 3 · 3 Hours to complete
Ensembles & Beyond
Module 4 · 2 Hours to complete
Fee Structure
Instructor
Associate Professor at the University of Michigan
Christopher Brooks is an Associate Professor in the School of Information at the University of Michigan, where he specializes in designing tools to enhance teaching and learning experiences in higher education. His research focuses on the application of learning analytics within human-computer interaction, utilizing methods from educational data mining, machine learning, and information visualization. Brooks has published extensively in these areas and is actively involved in directing the Educational Technology Collective, which includes postdoctoral scholars and students collaborating on innovative projects. He teaches various courses related to applied data science and has contributed to online education platforms such as Coursera. His work aims to leverage data to improve educational outcomes and foster better learning environments.
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4.7 course rating
20 ratings
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