University of Michigan - Introduction to Machine Learning in Sports Analytics
- Offered byCoursera
Introduction to Machine Learning in Sports Analytics at Coursera Overview
Duration | 13 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to Machine Learning in Sports Analytics at Coursera Highlights
- 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.
- Course 5 of 5 in the Sports Performance Analytics Specialization
- Intermediate Level Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.
- Approx. 13 hours to complete
- English Subtitles: English
Introduction to Machine Learning in Sports Analytics at Coursera Course details
- 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.
Introduction to Machine Learning in Sports Analytics at Coursera Curriculum
Machine Learning Concepts
Introduction
What is Machine Learning?
The Machine Learning Workflow
Our First Model: NHL Game Outcomes
Building the Logistic Regression Model
Considerations in Deploying The Model
Wrap Up
Help Us Learn More About You
Assignment 1 Programming Solution
Assignment 1
Support Vector Machines
Introduction to Support Vector Machines (SVMs)
Polynomial Support Vector Machines
Cross Validation
A Real World SVM Model: Boxing Punch Classification
(Optional) - An evaluation of wearable inertial sensor configuration and supervised machine learning models for automatic punch classification in boxing
Assignment 2 Programming Solution
Assignment 2
Decision Trees
Decision Trees
A Multiclass Tree Approach
Model Trees
Tuning and Inspecting Model Trees
Assignment 3 Programming Solution
UM Master of Applied Data Science (optional)
Assignment 3
Ensembles & Beyond
Ensembles
Additional Machine Learning Concepts
Baseball Hall of Fame Prediction
Baseball Hall of Fame Demonstration Part 1
Baseball Hall of Fame Demonstration Part 2
Free Deepnote Notebook Service
Putting Your Skills to the Test!
Post Course Survey
Assignment 4
Introduction to Machine Learning in Sports Analytics at Coursera Admission Process
Important Dates
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