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Artificial Intelligence

Computer Vision Essentials

4.46 (200 Ratings)


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About this course

Computer vision (CV) is a set of techniques that are used to help machines to “see” and comprehend what is contained in digital imagery such as photos and videos. It has varied applications such as in medical imaging, motion capture, surveillance, automating retail checkouts, and in optical character recognition. This course starts with the basic steps of digitizing images, sampling them and compressing them (quantization). It then covers various methods to work with images including classification, identification, detection, etc.


  • Computer Vision
  • CNN

Course Syllabus

Computer Vision Essentials

  • Agenda
  • Introduction
  • Computer Vision
  • Digital Image
  • Process
  • Intro to Pooling and Filters
  • Sampling
  • Image Processing and Filtering
  • Case Study 1
  • CNN
  • Why CNN?
  • Padding
  • Pooling and Filtering
  • Types of CNN Architectures
  • Intro to Transfer Learning
  • Working of Transfer Learning
  • Problem
  • Case Study 2

Course Certificate

Get Computer Vision Essentials course completion certificate from Great learning which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.

GL Academy Sample Certificate

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