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Computer Vision is the branch of Computer Science—particularly Machine Learning and AI—that has applications in many industries such as self-driving cars, robotics, augmented reality, face detection in law-enforcement agencies, and more. Essentially, it’s a robot analogue of human vision in which information about the environment is received by one or more video cameras and processed by a computer.

Computer Vision solves a lot of problems, making it important to learn. Some of its uses include advances in health technologies. Computer Vision algorithms can help automate tasks such as detecting cancerous moles in skin images or finding symptoms in x-ray and MRI scans.‎

Thanks to a need for quality inspection in vision-guided robotic systems, the market for Computer Vision is anticipated to rise to $17.4 billion by 2024. To reap the benefits of this in-demand field, learners can enjoy opportunities as Computer Vision Engineers, Computer Vision Software Engineers, Applied Research Scientists, Computer Vision Testing Engineers, Deep Learning Engineer, Computer Vision Data Scientist, and more.‎

Computer Vision courses offered through Coursera equip learners with knowledge in how computers see and interpret the world as humans do; core concepts of Computer Vision and human vision capabilities; key application areas of Computer Vision and Digital Image Processing; Machine Learning and AI basics; and more.

Lessons on Computer Vision are taught by Data Scientists, Software Engineers, and other specialists, and are administered via video lectures, readings, quizzes, hands-on projects, and more.‎

Before you begin studying computer vision, you’ll want to have a familiarity with mathematical analysis and linear algebra as well as a knowledge of Python syntax, the TensorFlow Deep Learning Framework, and OpenCV library. Any advanced experience in computer programming will benefit you as you learn computer vision, particularly if you understand application programming interfaces. You’ll also have an advantage in studying computer vision if you have knowledge of artificial intelligence and machine learning. Other mathematical skills that will help you include statistics and geometry. And a working knowledge of biological vision will help you have a better understanding of how computer vision works.‎

Computer vision is just a small slice of the quickly growing field of machine learning, and people with experience in artificial intelligence or machine learning, in general, are well suited for job opportunities in this discipline. Anyone with a background in machine learning can take their skills and specifically hone them for a computer vision career. Data scientists who have deep learning, data structure, and programming language knowledge have a foundation that will benefit them as they study computer vision. Any programmer or scientist who has experience with digital image processing computer programs and algorithms has grasped the basic fundamentals to embark on a career in computer vision.‎

Computer vision is an exciting field with possibilities in health care, app development, and data security, and if you’re interested in being on the leading edge of any of those industries, you may want to consider learning computer vision. Learning computer vision is right for anybody who wants to develop platforms and programming languages that allow computers to see the world the way humans do. You might find the idea of analyzing large numbers of images and building visual systems that read the data and context of those images for web-based apps and computer programs to recognize appeals to you, and if so, learning computer vision is likely right for you.‎