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PG Diploma in Computer Vision
Computer vision is one of the most widely studied sub-fields of computer science. It has several important applications, such as face detection, image searching, artistic image conversion and with the popularity of deep learning methods, many recent applications of computer vision are in self-driving cars, robotics, medicine, virtual reality and augmented reality. Sometimes computer vision tries to mimic human vision and uses data & statistical approaches or uses geometry to solve real-world problems.
Batch starts date

May 8, 2023

Program Duration

9 Months

Learning Format

Blended Learning

Program Fees


Course Overview

Computer vision contains a mix of programming, modeling, mathematics and is sometimes difficult to grasp so Airtics has designed this course to give a practical approach of learning computer vision with enough understanding of underlying theory, programming and algorithms to help in building stronger computer vision fundamentals. This course teaches how to create computer vision applications using standard tools such as OpenCV, Keras and TensorFlow. The various concepts taught in this course can be used across several domains from image editing apps to self-driving cars.

Training Key Features

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    What you will learn

    About UCAM

    Universidad Católica de Murcia (UCAM), founded in 1996, is a fully-accredited European University based out of Murcia, Spain. With learning centres in the Middle East and Southeast Asia, UCAM aims to provide students with the knowledge and skills to serve society and contribute to the further expansion of human knowledge through research and development.

    The university offers various courses, including 30 official bachelor’s degrees, 30 master’s degrees and ten technical higher education qualifications through its Higher Vocational Training Institute, in addition to its in-house qualifications and language courses. The programmes offered are distinguished in Europe and worldwide, with good graduate employability prospects as well.

    UCAM is accredited by ANECA (National Agency for Quality Assessment and Accreditation of Spain) and the Ministry of Education regarding 17 of its undergraduate degrees.

    Airtics Education’s PG Diploma Programs are certified by UCAM University.

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    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

    Pandas, NumPy, Seaborn, Matplotlib, Scikit, Tensorflow, OpenCV, Keras, Scikit Image

    Database Integrations

    Automated Machine Learning Models

    Supervised, Unsupervised

    Application And Use Cases

    CV helps in parking occupancy detection, traffic flow analysis, self-driving cars
    CV is used in banking for biometric recognition for authentication, fraud control, etc.
    CV helps automate harvesting, plant disease detection, crop and yield monitoring.
    Health Care
    Computer vision is used to analyze X-Ray, MRI, CT scans as accurately as human doctors
    CV with ML algorithm helps large-scale manufacturing in accurate defect detection
    CV tools helps teachers to conduct classes & examinations online.
    CV made it possible to self-checkout in supermarkets, do full inventory scans and notify stock-outs.


    • Bachelor’s Degree from a recognized University
    • Proficiency in English language


    • 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

    This module discuss the basics of Python programming language and explore how to setup Python environment to work with machine learning. Demonstrate different parts of a Python code such as keywords, variables, data types, statements, functions, loops, libraries and get familiarized with programming in python.
    • Learn basic concepts of Python
    • Acquire rudimentary skills to write programs in Python
    • Ability to use Python for Data Science & Machine learning
    • Get application-ready with essential Python libraries & tools
    Content Covered
    • Basic Python Programming
      • Variable and data types
      • Conditional statements
      • Loops
      • Functions
    • Essential Python libraries for data science
      • Pandas
      • Numpy
      • Scikit
    • Setting up Python for Machine Learning
    Mathematics have a significant role in the foundation for programming and this module is designed to help students master the mathematical foundation required for writing programs and algorithms for Artificial Intelligence and Machine Learning. The module covers three main mathematical theories: Linear Algebra, Statistics and Probability Theory.
    • Master the mathematical foundation required for writing programs
    • Learn mathematical and statistical foundations required for AI & ML
    • Acquire mathematical knowledge to build algorithms for data analysing
    • Apply statistical analysis techniques using essential softwares on data sets
    Content Covered
    • Linear Algebra
    • Statistics
    • Probability Theory
    • Statistical Tools (CSV, Excel)
    This module offers a guide to the parts of the Python programming language and its data oriented library ecosystem and tools that will equip students to become effective data analysts. The module focuses specifically on Python programming, libraries, and tools needed for data analysis. Essential Python libraries covered in this module are NumPy, pandas & matplotlib. NumPy provides the data structures, algorithms, and library glue needed for most scientific numerical data applications in Python. Pandas provide high-level data structures and functions that make working with structured or tabular data fast, easy, and expressive. Matplotlib libraries are used for producing plots and other two-dimensional data visualizations.
    • Acquire practical skills in data analyzing, handling & visualization using Python tools
    • Perform mathematical operations on a wide range of data using NumPy
    • Operate Pandas to sort through and rearrange data, run analyses, and build data frames
    • Ability to analyze by visualizing data with Matplotlib
    Content Covered
    • Python Programming for AI & ML
      • Essential Python libraries for data analysis
      • Data storage and manipulation by NumPy
      • Data Visualization using Matplotlib
      • Data Analysis with Pandas
      • Basic introduction to Sci-kit-learn
    This module provides an in-depth understanding of established methods of artificial intelligence and machine learning techniques that enable computers to learn without being explicitly programmed. The module discusses various parts of artificial intelligence, which include ML (Machine Learning), DL (Deep Learning), NLP (Natural language Processing), RL (Reinforcement learning), and DRL (Deep reinforcement learning), and aims to explain the real-world application of improved algorithms such as linear regression, k-NN, decision trees, random forest, etc. for machine learning by supervised, unsupervised and reinforcement learning.
    • Understand Artificial Intelligence and Machine Learning fundamentals
    • Demonstrate a comprehensive knowledge of the nature of the data and techniques used for pre-processing the data for machine  learning
    • Introduction to major machine learning algorithms like Classifiers (for image, spam, fraud), Regression (stock price, housing price, etc.), Clustering (unsupervised classifiers)
    • Demonstrate a deep critical understanding of algorithms and mathematics behind established ML approaches
    Content Covered
    • Introduction to Machine Learning & AI
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
    • Machine Learning Algorithms (Regression, Classifiers, Clustering)
    • Machine Learning Task (dataset, data cleaning, algorithm selection, training & testing model)
    This module begins by learning about numerical processing using the NumPy library, reading and changing photographs using the OpenCV library to open and deal with picture essentials, and gaining insight into using current deep learning network models like CNN & RNN. Comprehend image processing and apply various effects, including color mappings, mixing, thresholds, gradients, etc. Learners master video basics using OpenCV, including dealing with streaming video from a webcam. The module will overview Image Processing & Computer Vision using Python. It will cover how TensorFlow and deep learning can be used for computer vision applications. Learners will learn to develop techniques to help computers see and understand the content of digital images, such as photographs and videos, using CNN (Convolution Neural Network).
    • Understand the Basic python tools used for Computer Vision
    • Understand image processing python packages to enable them to write scripts for text pre-processing
    • Learn popular machine learning algorithms, Feature Selection, and Mathematical intuition behind it
    • Understand basic concepts and standard tools used in computer vision
    Content Covered
    • Core Python for computer vision
      • Strings
      • Regex
    • Machine Learning algorithms
      • Regression
      • KNN
      • SVM
    • Computer vision tools
      • Keras
      • TensorFlow
    This module will provide learners with knowledge and understanding of the application of machine learning methodologies to handle industrial difficulties, to a more extensive array of data mining and classification type activities. Learners will discover the machine learning algorithms by utilizing neural networks, k-means clustering, and support vector machines in computer vision to analyze data based on supervised, unsupervised, and partially supervised. Additionally covered in this module are, Tensor flow, Faster- RCNN-Inception-V2 model, and Anaconda software development environment utilized to recognize autos and individuals in pictures that provides insight into the usage of current deep learning network models like CNN
    • Concepts of deep learning to build artificial neural networks and traverse layers of data abstraction and get a solid understanding of deep learning
    • Develop and build fully automated CV algorithms USING YOLO
    • Develop the usage of Deep learning models like CNN and RNN
    • Gain insights about advancements in CV, AI, and Machine Learning techniques
    Content Covered
    • Introduction to Computer Vision (CV)
    • Deep Learning Network Models
    • Convolutional Neural Networks (CNNs)
    • Recurrent Neural Networks (RNNs)
    • Introduction to Keras Model Life-Cycle
    • Image Data Manipulation using Pillow Python library.
    • Convert Images to NumPy Arrays and Back

    The purpose of this module is to discuss and explain the role of Artificial Intelligence and Machine Learning in an organization and their influence on its overall performance and competence. Learners will be encouraged to pick a research/development project that displays their past learning in the AI & ML domain. It is meant to understand various aspects of AI, such as Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision, to name a few. It also endeavors to highlight the role and significance of AI & ML during the planning, decision-making, and implementation of change in the organization.

    Upon completing the module, the participants will have comprehensive knowledge and the ability to demonstrate their expertise in Artificial Intelligence and Machine Learning to potential employers or educational programs.


    • Conduct independent research and development within the context of an AL & ML project
    • Produce detailed documentation to a standard expected of a professional in the field of AI & ML
    • Communicate technical information clearly and succinctly to a broad, non-specialist audience
    • Apply knowledge of research principles and methods to plan and execute a research based industry project with a high level of personal autonomy and accountability

    Content Covered

    • Clarifying the terms of the research
    • Suggesting areas of reading
    • Apply the knowledge base and abilities taught throughout the course to a real-world scenario
    • The Problem, Understanding It, and Getting Data
    • Frame a business issue in a manner that can be solved with AI & ML
    • Apply Exploratory Data Analysis and Modeling
    • Identify the methodology or algorithm that will handle the proposed challenge
    • Reviewing the proposed methodology
    • Establishing a research timetable, including initial dates for further meetings between the student and supervisor
    • Advising students about appropriate standards & conventions concerning the assessment.
    • Providing means of contact in addition to tutorials
    • Educate learners to research and write their results and thoughts correctly, clearly, logically, and to a high-professional degree

    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…..

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      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…

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      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…

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      Capstone Projects

      Showcase your capabilities with real-world projects

      Bring Your Own Project
      Learn to solve a problem that you/your organization is facing using Computer Vision


      Choose From Curated Capstone Projects

      Face detection and identification

      Object detection for blood cells

      Medical image classification

      Career Support

      Frequently Asked Questions

      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.
      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
      The face recognition algorithm is basically the computer application that is used for tracking, detecting, identifying, or verifying 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 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 are 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?

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        Course Preview

        PG Diploma in Computer Vision

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