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Introduction to Machine Learning in Sports Analytics
  • Level Professional
  • Duration 13 hours
  • Course by University of Michigan
  • Offered by

About

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.

Modules

JupyterLab Notebooks for All Lectures

    • 1 Lab
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1 Labs

  • JupyterLab

Lesson

    • 7 Videos
    • 3 Readings
    • 1 Assignment
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1 Assignment

  • Assignment 1

7 Videos

  • 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

3 Readings

  • Help Us Learn More About You
  • Course Syllabus
  • Assignment 1 Programming Solution

Lesson

    • 4 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Assignment 2

4 Videos

  • Introduction to Support Vector Machines (SVMs)
  • Polynomial Support Vector Machines
  • Cross Validation
  • A Real World SVM Model: Boxing Punch Classification

2 Readings

  • (Optional) - An evaluation of wearable inertial sensor configuration and supervised machine learning models for automatic punch classification in boxing
  • Assignment 2 Programming Solution

Lesson

    • 4 Videos
    • 2 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Assignment 3

4 Videos

  • Decision Trees
  • A Multiclass Tree Approach
  • Model Trees
  • Tuning and Inspecting Model Trees

2 Readings

  • Assignment 3 Programming Solution
  • UM Master of Applied Data Science (optional)

Lesson

    • 5 Videos
    • 3 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Assignment 4

5 Videos

  • Ensembles
  • Additional Machine Learning Concepts
  • Baseball Hall of Fame Prediction
  • Baseball Hall of Fame Demonstration Part 1
  • Baseball Hall of Fame Demonstration Part 2

3 Readings

  • Free Deepnote Notebook Service
  • Putting Your Skills to the Test!
  • Post Course Survey

Auto Summary

"Introduction to Machine Learning in Sports Analytics" is a professional-level course designed for those interested in the intersection of data science, AI, and sports. This course offers a deep dive into supervised machine learning techniques, leveraging the Python scikit-learn toolkit and real-world athletic data to predict and analyze athletic outcomes. Guided by an expert instructor from Coursera, learners will explore methods such as support vector machines (SVM), decision trees, random forests, linear and logistic regression, and ensembles of learners. These techniques will be applied to data from professional sports leagues like the NHL and MLB, as well as data from wearable devices such as the Apple Watch and inertial measurement units (IMUs). Spanning a duration of 780 minutes, the course builds on previous specializations and ensures participants gain a comprehensive understanding of classification and regression techniques in the context of sports analytics. Ideal for professionals aiming to enhance their skills in data science and AI with a sports focus, this course is available under the Starter subscription plan on Coursera.

Christopher Brooks
Instructor

Christopher Brooks