OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.
Course image
Course image
Coursera logo

Machine Learning for Computer Vision

Matt Rich, Amanda Wang, Megan Thompson, Brandon Armstrong, and Isaac Bruss

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.

Read more

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.

Enroll now

Here's a deal for you

Save money when you learn with a deal that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until March 12, 2027
Save 40% Off 3 Months
Access thousands of top-rated learning programs with a Coursera Plus monthly subscription and take 40% off 3 months for a limited time.
Take
40%
off
Get offer

What's inside

Syllabus

Image Classification with Machine Learning
Image Classification Using Bag of Features
Evaluating Classification Models
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
In this 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

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Practical ml for computer vision with matlab

According to students, this course receives a largely positive reception, particularly for professionals in engineering and science. Learners frequently highlight the hands-on labs and real-world examples as its strongest features, enabling them to apply concepts directly. The clear explanations and well-structured modules on image classification and object detection are also highly praised. A key aspect is its deep integration with MATLAB, which is a significant advantage for those already familiar with the tool. However, some learners note a strong reliance on MATLAB may be a barrier for others. Furthermore, while the course purports to require 'some' prior image processing experience, many found it beneficial to have a more solid background to avoid struggling.
Course extensively uses MATLAB, a benefit for some, a hurdle for others.
"The MATLAB access was a huge plus, and for a practical application course, it served its purpose well."
"I found it a bit too focused on MATLAB, and less on the general ML concepts, which made it harder to transfer knowledge to other tools."
"I struggled a lot with this course. While the topics are important, the reliance on MATLAB was a barrier for me as I prefer Python."
Well-structured content and clear instruction delivery.
"The instructors were very clear, and the classification and object detection modules were well-structured."
"Excellent course! The explanations were clear, the labs were practical, and it directly addressed real-world problems."
"The material was well-organized, and the projects were truly insightful. It cemented my understanding of image classification and object detection."
Strong emphasis on hands-on application and real-world problem-solving.
"This course was a fantastic deep dive into applying ML for computer vision using MATLAB. The hands-on labs were incredibly helpful."
"As an engineer, I found the practical approach extremely valuable. The exercises reinforced the concepts perfectly."
"Very hands-on course, which I liked. The assignments were challenging but doable. Overall, a solid course for applying ML in vision."
More image processing experience is recommended than stated.
"While it says 'some prior image processing experience', I felt you needed more than 'some'. If you're a beginner, prepare to do a lot of extra reading."
"The pace was just right for someone with a basic understanding of image processing."
"I think the pre-requisite on image processing is accurate; if you skip that, you'll struggle. Not really for beginners despite what the intro might imply."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning for Computer Vision with these activities:
Review Image Processing Concepts
Refresh your knowledge of image processing concepts to strengthen your understanding of the course material.
Browse courses on Image Processing
Show steps
  • Review key concepts from your previous image processing course.
  • Read introductory articles or watch tutorials on image processing.
Create a Cheat Sheet for MATLAB
Solidify your understanding of MATLAB by creating a comprehensive cheat sheet for future reference.
Browse courses on MATLAB
Show steps
  • Identify the key commands, functions, and syntax related to image processing and machine learning in MATLAB.
  • Organize and format the information in a clear and concise manner.
Participate in Peer Study Groups
Collaborate with peers to strengthen your knowledge and understanding of the course material in a supportive environment.
Browse courses on Image Processing
Show steps
  • Find or organize a study group with other students taking the same course.
  • Meet regularly to discuss the lecture material, work on assignments, and clarify concepts.
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow Tutorials on Image Classification
Enhance your understanding of image classification techniques by following guided tutorials.
Browse courses on Image Classification
Show steps
  • Identify reputable online platforms or resources that provide tutorials on image classification using MATLAB.
  • Select tutorials that align with your skill level and learning goals.
  • Work through the tutorials step-by-step, taking notes and experimenting with the provided code.
Practice Coding Exercises
Enhance your programming skills and solidify your understanding of image classification and object detection algorithms through practice.
Browse courses on MATLAB
Show steps
  • Find online coding exercises or practice problems related to the course topics.
  • Attempt to solve the problems independently using MATLAB.
Develop a MATLAB Project
Apply your knowledge and skills to a practical project to gain valuable hands-on experience in computer vision.
Browse courses on MATLAB
Show steps
  • Identify a specific problem or application in the field of image processing or computer vision.
  • Design and implement a solution using MATLAB.

Career center

Learners who complete Machine Learning for Computer Vision will develop knowledge and skills that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineers develop and apply computer vision techniques to solve real-world problems in various industries such as manufacturing, healthcare, and autonomous vehicles. This course provides a comprehensive introduction to Machine Learning for Computer Vision, empowering learners with the skills and knowledge necessary to perform image classification and object detection tasks. By completing this course, individuals can gain a strong foundation in the field and enhance their capabilities as Computer Vision Engineers.
See salaries and explore the career path for Computer Vision Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models for various applications. This course helps build a foundation in Machine Learning for Computer Vision, providing Machine Learning Engineers with the skills to apply machine learning techniques to computer vision tasks. By gaining proficiency in image classification and object detection, Machine Learning Engineers can enhance their ability to develop and implement robust computer vision models.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
Data Scientists analyze and interpret large amounts of data to extract meaningful insights and make data-driven decisions. This course provides an introduction to Machine Learning for Computer Vision, enabling Data Scientists to expand their skill set and apply machine learning techniques to computer vision problems. By understanding image classification and object detection, Data Scientists can enhance their ability to derive valuable insights from visual data.
See salaries and explore the career path for Data Scientist
Robotics Engineer
Robotics Engineers design, build, and maintain robots for various applications such as manufacturing, healthcare, and space exploration. This course introduces the fundamentals of Machine Learning for Computer Vision, providing Robotics Engineers with the skills to develop computer vision algorithms for robots. By gaining expertise in image classification and object detection, Robotics Engineers can enhance the capabilities of robots to perceive and interact with their surroundings.
See salaries and explore the career path for Robotics Engineer
Computer Vision Researcher
Computer Vision Researchers conduct research and development in the field of computer vision, exploring new techniques and algorithms for image and video analysis. This course provides a solid foundation in Machine Learning for Computer Vision, enabling Computer Vision Researchers to enhance their understanding of machine learning techniques applied to computer vision tasks. By specializing in image classification and object detection, researchers can contribute to the advancement of computer vision technologies.
See salaries and explore the career path for Computer Vision Researcher
Software Engineer
Software Engineers design, develop, and maintain software applications for various industries. This course offers an introduction to Machine Learning for Computer Vision, providing Software Engineers with the skills to incorporate computer vision capabilities into their software applications. By gaining proficiency in image classification and object detection, Software Engineers can enhance the functionality and user experience of their software products.
See salaries and explore the career path for Software Engineer
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. This course provides a foundation in Machine Learning for Computer Vision, enabling Quantitative Analysts to apply machine learning techniques to financial data analysis. By specializing in image classification and object detection, Quantitative Analysts can enhance their ability to extract insights from visual data, leading to more informed investment decisions.
See salaries and explore the career path for Quantitative Analyst
Product Manager
Product Managers are responsible for defining the vision, roadmap, and features of technology products. This course offers an introduction to Machine Learning for Computer Vision, providing Product Managers with the knowledge to make informed decisions about incorporating computer vision into their products. By understanding image classification and object detection, Product Managers can develop products that meet the evolving needs of users in various industries.
See salaries and explore the career path for Product Manager
Data Analyst
Data Analysts collect, analyze, and interpret data to identify trends and insights. This course introduces Machine Learning for Computer Vision, enabling Data Analysts to expand their skillset and apply machine learning techniques to visual data analysis. By gaining expertise in image classification and object detection, Data Analysts can enhance their ability to extract meaningful insights from images and videos.
See salaries and explore the career path for Data Analyst
Business Analyst
Business Analysts identify and analyze business needs and requirements. This course provides an introduction to Machine Learning for Computer Vision, equipping Business Analysts with the knowledge to understand the potential applications of computer vision in various business contexts. By specializing in image classification and object detection, Business Analysts can contribute to the development of innovative solutions that leverage computer vision technologies.
See salaries and explore the career path for Business Analyst
Technical Writer
Technical Writers create and maintain documentation for technical products and systems. This course offers an introduction to Machine Learning for Computer Vision, providing Technical Writers with the knowledge to effectively communicate the concepts and applications of computer vision to technical audiences. By understanding image classification and object detection, Technical Writers can produce clear and informative documentation that helps users understand and utilize computer vision technologies.
See salaries and explore the career path for Technical Writer
UX Designer
UX Designers create user experiences for products and services. This course provides an introduction to Machine Learning for Computer Vision, enabling UX Designers to gain an understanding of how computer vision can enhance user interactions and experiences. By specializing in image classification and object detection, UX Designers can design user interfaces that seamlessly incorporate computer vision capabilities, leading to more intuitive and engaging user experiences.
See salaries and explore the career path for UX Designer
Marketing Analyst
Marketing Analysts analyze marketing data to evaluate the effectiveness of marketing campaigns and make data-driven decisions. This course introduces Machine Learning for Computer Vision, providing Marketing Analysts with the skills to incorporate computer vision into their data analysis and marketing strategies. By understanding image classification and object detection, Marketing Analysts can gain insights from visual data, leading to more targeted and effective marketing campaigns.
See salaries and explore the career path for Marketing Analyst
Project Manager
Project Managers are responsible for planning, executing, and delivering projects. This course offers an introduction to Machine Learning for Computer Vision, providing Project Managers with the knowledge to manage projects that involve computer vision technologies. By understanding image classification and object detection, Project Managers can effectively collaborate with technical teams and ensure the successful delivery of computer vision projects.
See salaries and explore the career path for Project Manager
Business Development Manager
Business Development Managers identify and develop new business opportunities for their organizations. This course provides an introduction to Machine Learning for Computer Vision, equipping Business Development Managers with the knowledge to explore new applications and markets for computer vision technologies. By specializing in image classification and object detection, Business Development Managers can identify potential partnerships and collaborations, leading to the expansion of their organization's business reach.
See salaries and explore the career path for Business Development Manager

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Machine Learning for Computer Vision.
Cover image
Computer Vision
Save
Provides a comprehensive overview of computer vision algorithms and techniques, covering a wide range of topics from image formation to object recognition. It valuable reference for both beginners and experienced practitioners in the field.
Computer Vision
Paperback
Check
Computer Vision
Kindle Edition
Check
Cover image
Probability and Statistics for Computer Science
Save
Provides a comprehensive and up-to-date introduction to computer vision, covering a wide range of topics from image formation to object recognition. It valuable resource for both beginners and experienced practitioners in the field.
Probability and Statistics for Computer Science
Paperback
$$$
Probability and Statistics for Computer Science
Kindle Edition
$$$
Pattern Recognition and Machine Learning
Save
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, from the basics of machine learning to the latest advances in the field. It valuable resource for both beginners and experienced practitioners in the field.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, from the basics of deep learning to the latest advances in the field. It valuable resource for both beginners and experienced practitioners in the field.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Hybrid Imaging and Visualization
Save
Provides a comprehensive overview of object detection with deep learning models. It covers a wide range of topics, from the basics of object detection to the latest advances in the field. It valuable resource for those who want to learn about object detection with deep learning.
Hybrid Imaging and Visualization: Employing Machine...
Kindle Edition
$$$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a practical introduction to machine learning for computer vision tasks. It covers a wide range of topics, from the basics of machine learning to the latest advances in computer vision. It valuable resource for those who want to learn how to use machine learning for computer vision tasks.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Concurrent NumPy in Python
Save
Provides a practical introduction to deep learning for computer vision tasks, covering topics such as convolutional neural networks, object detection, and image segmentation. It valuable resource for those who want to learn about the latest advances in computer vision.
Concurrent NumPy in Python: Faster NumPy With BLAS,...
Paperback
$$
Concurrent NumPy in Python: Faster NumPy With BLAS,...
Kindle Edition
$$
Cover image
Learning OpenCV 3
Save
Provides a practical introduction to computer vision using the OpenCV library. It covers a wide range of topics, from image processing to object detection and tracking. It valuable resource for those who want to learn how to use OpenCV for computer vision tasks.
Learning OpenCV 3: Computer Vision in C++ with the...
Paperback
$$$
Learning OpenCV 3: Computer Vision in C++ with the...
Kindle Edition
$$$
Cover image
Learning OpenCV 4 Computer Vision with Python 3
Save
Provides a practical introduction to computer vision using the OpenCV library. It covers a wide range of topics, from image processing to object detection and tracking. It valuable resource for those who want to learn how to use OpenCV for computer vision tasks.
Learning OpenCV 4 Computer Vision with Python 3
Paperback
$$
Learning OpenCV 4 Computer Vision with Python 3:...
Kindle Edition
$$
Cover image
Deep Learning For Dummies
Save
Provides a beginner-friendly introduction to computer vision. It covers a wide range of topics, from the basics of image processing to the latest advances in computer vision. It valuable resource for those who want to learn about computer vision without getting too technical.
Deep Learning For Dummies
Paperback
Check
Deep Learning For Dummies
Kindle Edition
Check

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are nine courses similar to Machine Learning for Computer Vision.
New Technologies for Business Leaders
Most relevant
Building AI Powered Chatbots Without Programming
Most relevant
SQL for Data Science
Most relevant
Intro to Digital Manufacturing with Autodesk Fusion 360
Most relevant
Deep Learning for Business
Most relevant
Python for Data Science, AI & Development
Most relevant
Data-driven Astronomy
Most relevant
Fundamentals of Scalable Data Science
Most relevant
Convolutional Neural Networks
Most relevant

Coursera Buyer Protection & Guarantee

Verified partner policies & purchase protections

Accredited University Partner
Free Trial & Audit

7-Day Free Trial & Free Audit

7-day free trial on monthly Coursera Plus subscriptions; free audit option available on most individual courses to explore video lectures and readings at no charge.

Return & Refund Policy

14-Day Refund Guarantee

14-day refund window on annual Coursera Plus subscriptions and single-course purchases prior to certificate issuance. Monthly subscriptions do not offer refunds once billed.

Buyer Protections

Verified Academic & Industry Partner

Accredited university and industry certifications (e.g. Stanford, Google, IBM, Yale), secure SSL payment processing, and verifiable digital certificate links.

Key Purchase Protections
14-day refund window on annual Coursera Plus and single courses
7-day free trial on monthly Coursera Plus subscriptions
Free course audit option for lectures and readings
Verified certificates from top universities & companies
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Level
Intermediate
Via
Coursera
Institution
MathWorks
Instructors
Matt Rich
Amanda Wang
Megan Thompson
Brandon Armstrong
Isaac Bruss
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова Bahasa Indonesia ไทย қазақша
This course belongs to a collection called Computer Vision for Engineering and Science. It's comprised of three courses:
  1. Object Tracking and Motion Detection with Computer Vision
  2. Machine Learning for Computer Vision (viewing)
  3. Introduction to Computer Vision
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
In this 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
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to Machine Learning for Computer Vision.
Enroll now
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser