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PG Diploma in Computer Vision

Computer vision is a highly researched subfield in computer science with a broad range of crucial applications, such as detecting faces, searching images, and converting images artistically. In recent years, due to the increasing popularity of deep learning techniques, computer vision has found many new applications in self-driving cars, robotics, medicine, virtual reality, and augmented reality. To solve real-world problems, computer vision uses either data and statistical approaches or geometry while sometimes trying to replicate human vision.

 

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Program Overview

Course Overview 

Computer vision is a field that involves programming, modelling, and mathematics. It can be challenging to comprehend, so we have developed a course that provides a practical approach to learning computer vision while also building a solid foundation in theory, programming, and algorithms. The course teaches students how to develop computer vision applications using widely-used tools like OpenCV, Keras, and Tensor Flow. The knowledge and skills acquired through this course can be applied across different domains, ranging from image editing apps to self-driving cars.

Flexible
9 Months
Certification
UCAM University, Spain
Blended Learning
Live classroom and Live online class.
Fees
$4250
+971 6 5310 843
(09:00am - 17:30pm)

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    Key Features

    Training Key Features

    • 144 hours of live instructor-led training
    • 3 industry-based projects, 6 assignments
    • 24/7 support and LMS Access
    • Hands-on experience with latest tools & applied projects
    • Live engagement classes by seasoned academics and professionals
    • Internship/Projects
    • Flexible timing for working professionals
    • EMI option

    What You Will Learn

    • You will learn essential mathematics such as linear algebra, vectors, matrices & probability theory 
    • You will learn programming with images using Python libraries like OpenCV, Keras & TensorFlow  
    • You will learn Computer vision techniques behind a variety of real-world applications
    • You will learn an elective of your choice: Advanced Python for CV or Deep learning with CV

    Skills Covered

    • Machine Learning
    • Deep Learning
    • Natural Language Processing
    • Reinforcement Learning
    • Computer Vision
    • Neural Networks

    Who Can Apply for the Course?

    Professionals who have a keen interest in AI

    Career starters interested in the field of AI

    Students aiming their career in AI and Machine learning

    Software and IT Professionals

    Individuals and professionals looking for a career change

    Engineers, Marketing, Healthcare, and Finance professionals aiming for career excellence

     

    Tools/ Frameworks/ Libraries

     

    Scripting Tools

    • Phyton, MySQL

     

    IDE Shell

    • Jupyterhub 

     

    Database Integrations

    • REST API

     

    Data Science Environment

    • Anaconda

     

    Tools/Libraries

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

     

    Automated Machine Learning Models

    • Supervised, Unsupervised

     

    Application And Use Cases

    • Transportation: CV helps in parking occupancy detection, traffic flow analysis, self-driving cars
    • Banking: CV is used in banking for biometric recognition for authentication, fraud control, etc.
    • Agriculture: 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
    • Manufacturing: CV with ML algorithm helps large-scale manufacturing in accurate defect detection
    • Education: CV tools helps teachers to conduct classes & examinations online.
    • Retail: CV made it possible to self-checkout in supermarkets, do full inventory scans and notify stock-outs.

    Eligibility

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

    Prerequisites

    • 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.

    Partners of this Programme

    UCAM University, Spain

    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.

    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.

    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.

    Why this Course ?

    Course

    Choosing a course of study where you are strongly inclined to pursue a European qualifying degree or for a skill set is a good start in pursuing your educational goals. At ECX, you would be empowered to lead the world.

    Place of Study

    To pursue your dream education, the key factor is that the students need ease in accessing the centre. At ECX, we come to your nearest city to overcome any challenges faced in commuting or travelling abroad without compromising on the quality of education.

    Affordable Fee

    Quality education abroad is highly expensive. At ECX, you benefit from enrolling on an affordable course with flexible payment options.

    Academic Support

    You get enrolled on a European degree, with blended teaching methodology and 360-degree academic assistance through our faculties with international standards for attaining a business management degree.

    Career Opportunities

    You become professionals in your respective field of study on completion of the degree as it brings in more of a realistic pursuit, thus transforming you with the better skill sets to approach the career market further.

    Course Resources

    For more detailed information about the course, please click on the links below.

    Download Brochure

    Program Details

    A three-year, practical-based degree applicable to a wide range of sectors. It equips you with the opportunity to develop the IT skills required to manage and improve your performance in a business environment.

    Learning Path

    Module 1
    Basics of Python
    Module description 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. Learning Outcomes LO1: Learn basic concepts of Python LO2: Acquire rudimentary skills to write programs in Python LO3: Ability to use Python for Data Science & Machine learning LO4: 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 Matplotlib Setting up Python for Machine Learning
    Module 2
    Mathematics & Statistics for Machine Learning & Artificial Intelligence
    Module Description 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. Learning Outcomes LO1: Master the mathematical foundation required for writing programs LO2: Learn mathematical and statistical foundations required for AI & ML LO3: Acquire mathematical knowledge to build algorithms for data analysing LO4: Apply statistical analysis techniques using essential softwares on data sets Content Covered Linear Algebra Statistics Probability Theory Statistical Tools (CSV, Excel)
    Module 3
    Python for Machine Learning
    Module description 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. Learning Outcomes LO1: Acquire practical skills in data analyzing, handling & visualization using Python tools LO2: Perform mathematical operations on a wide range of data using NumPy LO3: Operate Pandas to sort through and rearrange data, run analyses, and build data frames LO4: Ability to analyze by visualizing data with Matplotlib Contents 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
    Module 4
    Introduction to Machine Learning & Artificial Intelligence
    Module Description 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. Learning Outcomes LO1: Understand Artificial Intelligence and Machine Learning fundamentals LO2: Demonstrate a comprehensive knowledge of the nature of the data and techniques used for pre- processing the data for machine learning LO3: Introduction to major machine learning algorithms like Classifiers (for image, spam, fraud), Regression (stock price, housing price, etc.), Clustering (unsupervised classifiers) LO4: 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)
    Module 5
    Advanced Python for Computer Vision (CV)
    Course Description 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). Learning Outcomes L01. Understand the Basic python tools used for Computer Vision L02. Understand image processing python packages to enable them to write scripts for text pre-processing L03. Learn popular machine learning algorithms, Feature Selection, and Mathematical intuition behind it L04. 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
    Module 6
    Machine Learning for Computer Vision (CV)
    Course Description 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 Learning Outcomes L01.Concepts of deep learning to build artificial neural networks and traverse layers of data abstraction and get a solid understanding of deep learning L02. Develop and build fully automated CV algorithms USING YOLO L03. Develop the usage of Deep learning models like CNN and RNN L04. 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
    Module 7
    PG Level Project/Dissertation
    Module Description 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. Learning Outcomes: LO1: Conduct independent research and development within the context of an AL & ML project LO2: Produce detailed documentation to a standard expected of a professional in the field of AI & ML LO3: Communicate technical information clearly and succinctly to a broad, non-specialist audience LO4: 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

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