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Machine Learning for Computer Vision

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Machine Learning for Computer Vision

This course is part of multiple programs.

9,756 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.8

23 reviews

Beginner level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.8

23 reviews

Beginner level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Prepare data and create features for classifying images

  • Train & evaluate models to classify images using

  • Train & evaluate object detection machine learning models

  • Customize model training for different applications using cost matrices

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments

Taught in English

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  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

In the second course of the Computer Vision for Engineering and Science specialization, you will perform two of the most common computer vision tasks: classifying images and detecting objects. You will apply the entire machine learning workflow, from preparing your data to evaluating your results. By the end of this course, you’ll train machine learning models to classify images of street signs and detect material defects.

You will use MATLAB throughout this course.  MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your computer vision tasks.  You will be provided free access to MATLAB for the course duration to complete your work. To be successful in this specialization, it will help to have some prior image processing experience.  If you are new to image data, it’s recommended to first complete the Image Processing for Engineering and Science specialization.

What's included

6 videos6 readings4 assignments

6 videos•Total 38 minutes
  • Computer Vision for Engineering and Science•3 minutes
  • Introduction to Machine Learning for Computer Vision•3 minutes
  • The Machine Learning Workflow•4 minutes
  • Introduction to Classification Models•7 minutes
  • Preparing Your Images for Classification•7 minutes
  • Training Image Classification Models•13 minutes
6 readings•Total 85 minutes
  • Meet Your Instructors•5 minutes
  • Course files and MATLAB•5 minutes
  • Glossary of Common Terms•15 minutes
  • Preparing the Concrete Images for Classification•20 minutes
  • Optimizing Model Hyperparameters•30 minutes
  • The Upcoming Assessments•10 minutes
4 assignments•Total 80 minutes
  • Graded Quiz: Preparing Images for Classification•30 minutes
  • Graded Quiz: Classifying Images•30 minutes
  • Concept Check: Introduction to Machine Learning•10 minutes
  • Concept Check: Introduction to Classification•10 minutes

What's included

2 videos2 readings3 assignments1 app item1 discussion prompt

2 videos•Total 13 minutes
  • Introduction to Bag of Features•4 minutes
  • Classifying Images With Bag of Features•8 minutes
2 readings•Total 55 minutes
  • Practice Using Bag of Features•20 minutes
  • Project: Introduction to Ground Cover Classification•35 minutes
3 assignments•Total 45 minutes
  • Graded Quiz: Bag of Features•20 minutes
  • Concept Check: Introduction to Bag of Features•15 minutes
  • Practice Quiz: Practice Using Bag of Features•10 minutes
1 app item•Total 10 minutes
  • Project: Ground Cover Classification with Different Classification Models•10 minutes
1 discussion prompt•Total 10 minutes
  • Project: Ground Cover Classification with Different Classification Models•10 minutes

What's included

3 videos2 readings2 assignments

3 videos•Total 25 minutes
  • Evaluating Classification Models•12 minutes
  • Evaluating Classification Models in MATLAB•7 minutes
  • Common Issues in Image Classification•7 minutes
2 readings•Total 70 minutes
  • Common Issues in Image Classification: A Reference•10 minutes
  • Project Introduction: Classifying Traffic Signs•60 minutes
2 assignments•Total 45 minutes
  • Project: Classifying Traffic Sign Images•30 minutes
  • Concept Check: Evaluating Classification Models•15 minutes

What's included

4 videos4 readings3 assignments

4 videos•Total 15 minutes
  • Object Detection with Machine Learning•7 minutes
  • Labeling your Images for Machine Learning•4 minutes
  • Introduction to the Object Detection Project•2 minutes
  • Summary of Machine Learning for Computer Vision•2 minutes
4 readings•Total 57 minutes
  • Object Detection in MATLAB•20 minutes
  • Beginning the Wood Knots Detection Project•5 minutes
  • Extra Credit: Removing Redundant Detections•30 minutes
  • What's Next?•2 minutes
3 assignments•Total 105 minutes
  • Project: Wood Knots Detection Step 1•45 minutes
  • Project: Wood Knots Detection Step 2•30 minutes
  • Project: Wood Knots Detection Step 3•30 minutes

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Instructors

Instructor ratings
4.8 (7 ratings)
MathWorks
9 Courses•48,659 learners

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SV
·

Reviewed on Mar 25, 2025

Excellent course to learn ML fundamentals in computer vision field.

Frequently asked questions

Yes. A free license is available to learners enrolled in the course. You must have a computer capable of running MATLAB. You can view the system requirements here.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Financial aid available,