Advanced Program in Computer Vision
The Master of Computer Vision program provides you with the technical skills and domain knowledge needed to succeed in this fast-growing industry. This involves acquiring, processing, analyzing and understanding images, videos, 3D data and other types of high-dimensional data from the real world employing the latest machine learning techniques.

Monthly Recurring Batch

Program Duration

12 Months

Learning Format

Blended Learning

Program Fees


Course Overview

This scientific field studies how computers can be used to automatically understand and interpret visual imagery. It aims to mimic the astounding capabilities of the human visual cortex using machine vision algorithms. It studies how an image is created, the geometry of the 3D world, and high-level tasks such as object recognition, object detection, and tracking, image segmentation, and action recognition. Computer vision has important applications in augmented/virtual reality, autonomous cars, service robots, biometrics and forensics, remote sensing, and security and surveillance.

 This specialization presents the first comprehensive treatment of the foundations of computer vision. It focuses on the mathematical and physical underpinnings of vision and has been designed for learners, practitioners, and researchers who have little or no knowledge of computer vision.

Training Key Features

For More Information

    What you will learn

    About Acacia University Extension (AUX)

    AUX, an extension of the prestigious Acacia University, USA, has initiated various programs in partnership with Airtics Education. The wide range of programs aims to upskill millions of students in trending technologies through a blend of theoretical and hands-on knowledge and are taught by leading academicians.

    Upon completion of these programs, you will receive an Advanced Certification from AUX.

    Click to Zoom

    Skills Covered

    Machine Learning

    Deep Learning

    Natural Language processing

    Reinforcement Learning

    Computer Vision

    Neural Networks

    Who Can Apply for the Course?

    Tools/ Frameworks/ Libraries

    Scripting tools

    Data science environment

    IDE shell

    Data Analytics Libraries

    Database Integrations

    Automated Machine Learning Models

    Supervised, Unsupervised

    Application And Use Cases

    Facebook tags
    Medicine and Healthcare


    Firm knowledge of software fundamentals with data structures, linear programming and architecture is beneficial.


    • Intermediate to advanced Python experience. You are familiar with object-oriented programming. You can write nested for loops and can read and understand code written by others.
    • Intermediate statistics background. You are familiar with probability.
    • You have seen or worked with a deep learning framework like TensorFlow, Keras, or PyTorch before.

    Course Modules

    Topics Covered: Python Libraries, Machine Learning, Artificial Neural Networks, Image Processing and OpenCV, Convolutional Neural Networks, Object Detection Techniques

    • basics, conditional statements, loops
    • lists, tuples, dictionaries, sets
    • functions
    • Hands on Numpy
    • Hands on Pandas
    • Hands on Matplotlib and Seaborn
    • Data cleaning and preprocessing
    • Supervised machine learning
    • Unsupervised machine learning
    • Hands on Use case
    • Introduction to Artificial Neural Networks
    • Introduction to Deep Neural Networks
    • Hands-on Usecase using tensorflow and keras
    • Fetch and display images
    • flip, resize, translate, rotate, crop images
    • Histogram equalization, Thresholding and blurring
    • Introduction to convolutional Neural networks
    • Image classification using CNN
    • Image Segmentation techniques
    • YOLO
    • Masked RCNN
    • SSD

    Interested in This Program? Secure your spot now.

    The application is free and takes only 5 minutes to complete.

      Student Reviews

      Veeraiah Yadav Doddaka
      IT Manager, Samsung

      I choose to learn Data Science and explored many options on which institute to join, among that what I found is Airtics as the best in terms of the course curriculum/on line content they designed and most…..

      Read More

      Prasad Joshi
      RF Optimization Engineer, Nokia

      I was a Data Science student at Airtics Education, which helped build a solid data science background and sharpen my programming skills…

      Read More

      Mohamed Hanan
      Procurement Assistant

      The program in Data Science offered by Airtics Education is rigorous and has provided me with a greater understanding of the data science world…

      Read More

      Capstone Projects

      Face detection and identification

      Object detection for blood cells

      Medical image classification

      Career Support

      Frequently Asked Questions

      With that in mind, if you’re interested in becoming a computer vision engineer, you’ll need to have a strong knowledge of mathematics, specifically data science, calculus, and linear algebra
      Computer Vision uses images and videos to understand a real-world scene. Just like Humans use eyes for capturing light, receptors in the brain for accessing it, and the visual cortex for processing it. Similarly, a computer understands images, videos, or a real-world scenario through machine learning algorithms and AI self-learning programming.
      The face recognition algorithm is basically the computer application that is used for tracking, detecting, identifying, or verifying the human faces simply from the image or the video that has been captured using the digital camera.

      We use sampling and Quantization to convert analog images to digital images. An image has two things.

      1. Coordinates

      Digitizing of coordinates is called Sampling. That is, converting the coordinates of the analog images to the digital images.

      2. Intensity/Amplitude

      Digitizing of Amplitude or Intensity is called Quantization. That is converting the Amplitude or Intensity of an analog image to a digital image.

      Computer vision(CV) is so amazing because it grows from traditional vision to AI vision tasks. Also when deep learning has evolved the complexities of traditional computer vision tasks solved in a fraction of time. So in the era of next-generation destructive technologies and applications computer vision plays a very important role in solving complex problems in engineering, society, and of course the whole planet.

      What is included in this course?

      I’m Interested in This Program

        By providing your contact details, you agree to our Terms of Use & Privacy Policy

        Course Preview

        Advanced Program in Computer Vision

          By providing your contact details, you agree to our Terms of Use & Privacy Policy