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.
Ce cours fait partie de la Spécialisation Sports Performance Analytics
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À propos de ce cours
Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.
Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.
Offert par
Programme de cours : ce que vous apprendrez dans ce cours
Machine Learning Concepts
Support Vector Machines
Decision Trees
Ensembles & Beyond
Avis
- 5 stars69,23 %
- 4 stars23,07 %
- 2 stars7,69 %
Meilleurs avis pour INTRODUCTION TO MACHINE LEARNING IN SPORTS ANALYTICS
Outstanding course! Really interesting and tutor was really enthusiastic which kept the videos and assessments easy to work through.
Very hands-on course, I could understand all techniques available to model sports.
À propos du Spécialisation Sports Performance Analytics

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