Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Introduction to Machine Learning in Sports Analytics is an intermediate-level online course provided by Coursera which is one of the leading online course providers. The online certification programme will guide the learners on how to use machine learning in sports analytics. Offered by Coursera, Introduction to Machine Learning in Sports Analytics Certification Course is the last course in the Sports Performance Analytics Specialization offered by the University of Michigan.
Introduction to Machine Learning in Sports Analytics Certification Syllabus will discuss the machine learning techniques, the prediction of athletic outcomes with machine learning, and many more. Introduction to Machine Learning in Sports Analytics Certification by Coursera can be taken by learners who have familiarity with Python.
The Highlights
- Provided by Coursera
- Offered by the University of Michigan
- Self-Paced Learning Option
- 100% Online Course
- Intermediate level course
- Around 12 Hours to Complete
- Flexible Deadlines
- Shareable Certificate
- Financial Aid Available
Programme Offerings
- English videos with multiple subtitles
- Shareable Certificate
- Financial aid available
- Shareable Certificates
- Self-Paced Learning Option
- Course Videos & Readings
- practice quizzes
- Graded Assignments with peer feedback
- graded Quizzes with feedback.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | UM–Ann ArborCoursera |
The fees for the course Introduction to Machine Learning in Sports Analytics is -
| Head | Amount in INR |
| 1 month | Rs. 3,277 |
| 3 month | Rs. 6,554 |
| 6 month | Rs. 9,831 |
What you will learn
By the end of the Introduction to Machine Learning in Sports Analytics Training, the candidates will understand:
- Application of ML algorithms to foresee the outcomes of the sport.
- Machine learning algorithms
- Support vector machines (SVM)
- Decision trees
- Random forest
- Linear and logistic regression
Who it is for
Introduction to Machine Learning in Sports Analytics Classes is highly recommended for professionals like ML Engineer, Data Analyst and many more.
Admission Details
Step 1 -Browse the official URL
https://www.coursera.org/learn/machine-learning-sports-analytics
Step 2 -Start learning the online programme ‘Introduction to Machine Learning in Sports Analytics’ by choosing the option ‘Enroll’.
The Syllabus
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
Readings
- Help Us Learn More About You
- Course Syllabus
- Assignment 1 Programming Solution
Quiz
- Assignment 1
Ungraded Lab
- JupyterLab
Videos
- Introduction to Support Vector Machines (SVMs)
- Polynomial Support Vector Machines
- Cross Validation
- A Real World SVM Model: Boxing Punch Classification
Readings
- (Optional) - An evaluation of wearable inertial sensor configuration and supervised machine learning models for automatic punch classification in boxing
- Assignment 2 Programming Solution
Quiz
- Assignment 2
Videos
- Decision Trees
- A Multiclass Tree Approach
- Model Trees
- Tuning and Inspecting Model Trees
Readings
- Assignment 3 Programming Solution
- UM Master of Applied Data Science (optional)
Quiz
- Assignment 3
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
Readings
- Free Deepnote Notebook Service
- Putting Your Skills to the Test!
- Post Course Survey
Quiz
- Assignment 4
Instructors
UM–Ann Arbor Frequently Asked Questions (FAQ's)
he online certification programme can be pursued by the learner in two different enrolment methods; they are audit mode and enrolling by paying the fee prescribed by Coursera.
The online course is developed and presented by Christopher Brooks, the assistant professor at the School of Information.
Yes, Coursera offers the learners facing difficulty in making the payment of the fee financial aid based on their financial background.
The course can be completed by the students within about 13 hours. However, the learners can take the course as per their time and convenience as it is designed in a flexible nature.
Coursera will award the learners with a shareable certificate only if they enrolled by paying the fee.