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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.
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/m0gkyc/machine
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What's inside
Syllabus
Image Classification with Machine Learning
Image Classification Using Bag of Features
Evaluating Classification Models
Read more
Syllabus
Image Classification with Machine Learning
Image Classification Using Bag of Features
Evaluating Classification Models
Read more
Object Detection with Machine Learning
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save Machine Learning for Computer Vision to your list so you can find it easily later:
Save
Save
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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.
Review Image Processing Concepts
Show steps
Refresh your knowledge of image processing concepts to strengthen your understanding of the course material.
Browse courses on
Image Processing
Show steps
Show steps
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
Show steps
Solidify your understanding of MATLAB by creating a comprehensive cheat sheet for future reference.
Browse courses on
MATLAB
Show steps
Show steps
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
Show steps
Collaborate with peers to strengthen your knowledge and understanding of the course material in a supportive environment.
Browse courses on
Image Processing
Show steps
Show steps
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
Show steps
Enhance your understanding of image classification techniques by following guided tutorials.
Browse courses on
Image Classification
Show steps
Show steps
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
Show steps
Enhance your programming skills and solidify your understanding of image classification and object detection algorithms through practice.
Browse courses on
MATLAB
Show steps
Show steps
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
Show steps
Apply your knowledge and skills to a practical project to gain valuable hands-on experience in computer vision.
Browse courses on
MATLAB
Show steps
Show steps
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 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.
Machine Learning 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.
Data Scientist
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.
Robotics Engineer
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.
Computer Vision Researcher
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.
Software Engineer
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.
Quantitative Analyst
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.
Product Manager
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.
Data Analyst
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.
Business 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.
Technical Writer
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.
UX Designer
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.
Marketing Analyst
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.
Project Manager
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.
Business Development 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.
For more career information including salaries, visit:
OpenCourser.com/course/m0gkyc/machine
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/m0gkyc/machine
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Level
Intermediate
Via
Coursera
Institution
MathWorks
Instructors
Matt Rich
Amanda Wang
Megan Thompson
Brandon Armstrong
Isaac Bruss
Language
English
Transcripts
Español
Français
Português
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This course belongs to a
collection
called
Computer Vision for Engineering and Science. It's comprised of three courses:
- Object Tracking and Motion Detection with Computer Vision
- Machine Learning for Computer Vision (viewing)
- Introduction to Computer Vision
Good to know
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
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