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Introduction to Machine Learning in Sports Analytics
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Introduction to Machine Learning in Sports Analytics

This course is part of Sports Performance Analytics.

Course Cost

Free course

Intermediate

Skill Level

11 Hours

Self-paced lessons

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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4.7

4,044 Enrolled

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English

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olive-leaves-logo

4.7

4,044 Enrolled

olive-leaves-logo

English

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

Machine Learning
Sports Analytics
Python Programming
Scikit-learn
SVM
Decision Trees
Random Forest
Classification
Regression Analysis
Ensemble Methods

This course includes:

4.7 Hours PreRecorded video

4 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

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Get a Completion Certificate

Share your certificate with prospective employers and your professional network on LinkedIn.

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Certificate
Certificate

Get a Completion Certificate

Share your certificate with prospective employers and your professional network on LinkedIn.

CREATED BY

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PROVIDED BY

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Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

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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

Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Sports Performance Analytics

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Introduction to Machine Learning in Sports Analytics

Intermediate

Skill Level

11 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.