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Professional Diploma in Computer Vision Fundamentals

Professional Diploma in Computer Vision Fundamentals

₹10,000.00 ₹5,000.00

50% fee concession will end in

To provide learners with fundamental knowledge and practical skills in Computer Vision techniques for enabling machines to interpret, analyze, and process visual information.

Description

Course Name: Professional Diploma in Computer Vision Fundamentals

Global Occupational Skill Standard – GOSS ID: GOSS/PD-AID-CVF-023

Eligibility: 10+2 or equivalent

Language Type: English

Course Type: Self-Paced Online Learning

Objective: The Professional Diploma in Computer Vision Fundamentals aims to equip learners with the knowledge and practical skills required to understand and apply computer vision concepts for AI-based image and video analysis. The course focuses on image processing, visual recognition, feature extraction, deep learning approaches, and real-world computer vision applications to develop intelligent visual solutions.

Duration: 6 Months / 370 Hours

Steps to become a GSDCI Certified Professional:

Step 1: Select your Course you want to pursue.

Step 2: Click on get Enroll Now tab, new pop up window will open.

Step 3: Click on pay Course fee, you will be redirected to billing details page.

Step 4: Fill your details and click on pay course fee, you will be redirected to payment gateway, pay fee by any available options like Card (Debit/Credit), Wallet, Paytm, Net banking, UPI and Google pay.

Step 5: You will get Login Credentials of Online E-Books and Online assessment link on your email id, within 48 hrs of payment.

Step 6: After completion of the online assessment, the Digital(PDF) copy of your Diploma & Marksheet will be sent to your registered email ID within 7 days.

Course Modules:

Module 1: Fundamentals of Computer Vision: Introduction to Computer Vision, History and Evolution of Computer Vision, Relationship Between AI Machine Learning and Computer Vision, Computer Vision Concepts and Terminology, Applications of Computer Vision, Image and Video Data Fundamentals, Computer Vision Pipeline, Challenges in Computer Vision, Computer Vision Tools and Technologies, Future Trends in Computer Vision.

Module 2: Image Processing Fundamentals: Digital Image Concepts, Image Representation and Formats, Image Acquisition Techniques, Image Enhancement Methods, Image Filtering Techniques, Image Transformation Operations, Image Segmentation Basics, Feature Extraction Methods, Image Compression Techniques, Image Processing Libraries and Tools.

Module 3: Machine Learning for Computer Vision: Introduction to Machine Learning in Computer Vision, Feature-Based Learning Methods, Image Classification Techniques, Object Recognition Concepts, Pattern Recognition, Support Vector Machines for Vision, Decision Tree Applications, Model Training and Evaluation, Computer Vision Datasets, Machine Learning-Based Vision Applications.

Module 4: Deep Learning for Computer Vision: Introduction to Deep Learning in Vision, Artificial Neural Networks, Convolutional Neural Networks (CNN), CNN Architectures, Transfer Learning Techniques, Image Classification Using Deep Learning, Object Detection Models, Image Segmentation with Deep Learning, Data Augmentation Techniques, Deep Learning Frameworks for Computer Vision.

Module 5: Advanced Computer Vision Applications: Face Detection and Recognition, Object Tracking Systems, Optical Character Recognition (OCR), Medical Image Analysis, Autonomous Vehicle Vision Systems, Video Analytics, Augmented Reality Applications, Industrial Computer Vision, Generative AI for Images, Real-World Computer Vision Case Studies.

Module 6: Computer Vision Deployment, Security and Capstone Project: Computer Vision Model Deployment, Cloud-Based Vision Services, Computer Vision APIs, Model Optimization Techniques, Real-Time Vision Applications, Computer Vision Security and Privacy, Ethical Issues in Computer Vision, Performance Monitoring and Improvement, Capstone Computer Vision Project Development, Final Project Presentation and Evaluation. 

GSDCI Online Assessment Detail:

  • Duration- 120 minutes.
  • Number of Questions- 60.
  • Number of Questions from each module: 10.
  • Language: English.
  • Exam Type: Multiple Choice Questions.
  • Maximum Marks- 600, Passing Marks Above 40%.
  • There is no negative marking in any module.
How Students will be Graded:
S.No. Marks Grade
1 91-100 O (Outstanding)
2 81-90 A+ (Excellent)
3 71-80 A (Very Good)
4 61-70 B (Good)
5 51-60 C (Average)
6 41-50 P (Pass)
7 0-40 F (Fail)

Benefits of the Professional Diploma

1. Globally Recognized Professional Diploma – Earn a professionally designed diploma that enhances your profile and demonstrates industry-relevant knowledge. Widely valued by employers, organizations, and institutions across multiple sectors.

2. Quality Learning with ISO-Certified Standards – Our training programs are developed under ISO-certified quality management practices, ensuring a structured, consistent, and high-quality learning experience.

3. Enhanced Career Opportunities – Strengthen your resume, improve your professional skills, and increase your potential for employment, career advancement, and new job opportunities.

4. Issued by a Non-Profit Skill Development Organization – The diploma is awarded by a mission-driven organization dedicated to promoting skill development, vocational education, and lifelong learning.

5. Comprehensive Learning Resources – Access quality study materials, e-books, practice assessments, mock tests, and learner support to help you successfully complete the program.

6. Secure Online Verification – Every diploma and marksheet is issued with a unique Enrolment ID and QR Code, enabling employers, institutions, and other stakeholders to instantly verify the authenticity of your credential online.

7. Lifetime Validity – The Professional Diploma is issued with lifetime validity and does not require periodic renewal, allowing you to showcase your achievement throughout your career.

8. Flexible Self-Paced Learning – Learn anytime and anywhere at your own pace, making it easy to balance your studies with work, business, or personal commitments.

9. Industry-Relevant Curriculum – The curriculum is designed to focus on practical knowledge, current industry trends, and real-world applications to improve workplace readiness.

10. Skill-Based Professional Qualification – Demonstrates your commitment to continuous professional development and validates your practical knowledge and skills in the subject area.

Job Opportunities after Professional Diploma in Computer Vision Fundamentals

Graduates of this program gain practical knowledge in computer vision concepts, image processing, deep learning applications, object detection, image recognition, visual data analysis, AI-based vision systems, and automation technologies, preparing them for roles in artificial intelligence, automation, analytics, and technology-driven industries.

Key Career Options: Computer Vision Associate, Computer Vision Engineer, AI Application Developer, Junior Machine Learning Engineer, Image Processing Specialist, Deep Learning Associate, AI Vision System Developer, Machine Learning Analyst, Data Analyst with Computer Vision Skills, Robotics Vision Specialist, AI Solutions Consultant, Computer Vision Research Assistant, Freelance AI Specialist.

Salary Range (India):
Entry-level: ₹3–6 LPA
Mid-level: ₹6–12 LPA
Senior-level: ₹12–25+ LPA (higher with advanced computer vision expertise, deep learning knowledge, programming skills, industry experience, and specialized AI projects)

Industries Hiring Graduates: Information Technology Companies, Software Development Firms, Artificial Intelligence Companies, Robotics & Automation Industries, Healthcare Technology, Manufacturing Industries, Automotive Technology, Security & Surveillance Solutions, E-commerce Platforms, Research Organizations, and Consulting Firms.

Skills Developed: Computer vision fundamentals, image processing techniques, object detection, image classification, facial recognition concepts, deep learning applications, AI vision tools, visual data analysis, automation using AI, machine learning basics, programming fundamentals, problem-solving skills, and implementation of AI-based vision solutions.

Graduates can progress to roles like Computer Vision Engineer, AI Vision Specialist, Machine Learning Engineer, Deep Learning Consultant, AI Solutions Architect, or start their own AI-based automation and computer vision services with industry experience, advanced certifications, and practical project expertise.

Course Rating

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Based on 57 student ratings

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