Introduction to Machine Learning in Sports Analytics

Introduction to Machine Learning in Sports Analytics

This course is part of Sports Performance Analytics Specialization

Instructor: Christopher Brooks

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4 modules

Gain insight into a topic and learn the fundamentals.

27 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

27 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Gain an understanding of how classification and regression techniques can be used to enable sports analytics across athletic activities and events.

Skills you'll gain

Tools you'll learn

Details to know

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Assessments

4 assignments

Taught in English

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This course is part of the Sports Performance Analytics Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 4 modules in this course

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 week will introduce the concept of machine learning and describe the four major areas of places it can be used in sports analytics. The machine learning pipeline will be discussed, as well as some common issues one runs into when using machine learning for sports analytics.

What's included

7 videos3 readings1 assignment1 ungraded lab

7 videos Total 75 minutes

Introduction 3 minutes
What is Machine Learning? 8 minutes
The Machine Learning Workflow 16 minutes
Our First Model: NHL Game Outcomes 20 minutes
Building the Logistic Regression Model 6 minutes
Considerations in Deploying The Model 20 minutes
Wrap Up 1 minute

3 readings Total 30 minutes

Help Us Learn More About You 10 minutes
Course Syllabus 10 minutes
Assignment 1 Programming Solution 10 minutes

1 assignment Total 60 minutes

Assignment 1 60 minutes

1 ungraded lab Total 10 minutes

JupyterLab 10 minutes

In this week students will learn how Support Vector Machines (SVM) work, and will experience these models when looking at both baseball and wearable data. Coming out of the week students will have experience building SVMs with real data and will be able to apply them to problems of their own.

What's included

4 videos2 readings1 assignment

4 videos Total 51 minutes

Introduction to Support Vector Machines (SVMs) 16 minutes
Polynomial Support Vector Machines 11 minutes
Cross Validation 9 minutes
A Real World SVM Model: Boxing Punch Classification 15 minutes

2 readings Total 130 minutes

(Optional) - An evaluation of wearable inertial sensor configuration and supervised machine learning models for automatic punch classification in boxing 120 minutes
Assignment 2 Programming Solution 10 minutes

1 assignment Total 60 minutes

Assignment 2 60 minutes

This week will focus on interpretable methods for machine learning with a particular focus on decision trees. Students will learn how these models work in general, and see special uses of decision trees in combination with regression methods. In this week students will come to better understand how the python sklearn toolkit can be used for a breadth of supervised learning tasks.

What's included

4 videos2 readings1 assignment

In this week of the course students will learn how many different models can be used together through ensembles, including the random forest method as a common use, as well as more general methods available in sklearn such as stacking and bagging. By the end of this week students will have a broad understanding of how methods such as SVMs, decision trees, and logistic regression can be used together to solve a problem with increasing performance.