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Certification in Computer Vision
Training Express Ltd

Updated 2026 | 63 Modules Instructor Lead Video Classes | FREE PDF & Hard Copy Certificate | Lifetime Access

Summary

Price
£19 inc VAT
Study method
Online, On Demand 
Duration
6.7 hours · Self-paced
Qualification
No formal qualification
CPD
10 CPD hours / points
CPD What's this?
Certificates
  • Digital certificate - Free
  • Hard copy certificate - Free
  • Reed Courses Certificate of Completion - Free
Additional info
  • Tutor is available to students

Add to basket or enquire

Overview

Welcome to the Certification in Computer Vision course. As your instructor, I’ve designed this comprehensive programme to walk you through the foundations, core techniques, and cutting-edge developments in the field of Computer Vision. Whether you're just starting or looking to enhance your expertise, this course combines theory with practical applications to help you build real-world skills.

We’ll begin with the fundamentals of Computer Vision and move into key topics such as pattern recognition, image processing, object detection, image segmentation, and deep learning-based classification. You’ll get hands-on experience using Python libraries like OpenCV and TensorFlow, and you’ll explore advanced concepts like YOLO, Mask R-CNN, and image-to-image translation with GANs. Toward the end, you’ll work on a capstone project that puts your knowledge to the test—building a real-world object detection and classification system.

If you're looking to build a strong foundation in Computer Vision, stay updated with current trends, and gain practical coding experience, this course will guide you step-by-step.

Learning Outcomes

  • Understand key components of computer vision systems and methods.
  • Learn Python tools for image processing and operations.
  • Apply techniques in image segmentation and classification.
  • Use YOLO and R-CNN for object detection models.
  • Perform object tracking and scene classification tasks.
  • Explore image-to-image translation using GANs and Pix2Pix.

Key Features :

  • Certified by CPD
  • Top-notch video lessons
  • FREE PDF & Hard Copy Certificate
  • Entirely online, interactive course with audio voiceover
  • Self-paced learning and laptop, tablet, and smartphone-friendly
  • 24/7 Learning Assistance
  • Discounts on bulk purchases

Certificates

CPD

10 CPD hours / points
Accredited by CPD Quality Standards

Curriculum

1
section
65
lectures
6h 40m
total

    • 1: 1. Introduction and study plan Preview 03:22
    • 2: 02 Module 1.1.1 Overview of Computer Vision 05:21
    • 3: 03 Module 1.1.2 Key Components of Computer Vision 06:41
    • 4: 04 Model 1.2.3 Pattern Recognition 12:18
    • 5: 05 Module 1.1.4 Technique and Algorithms 04:56
    • 6: 06 Module 1.1.5 Challenges in Computer Vision 07:58
    • 7: 07 Module 1.1.6 Basic of Image Processing with Python 04:16
    • 8: 08 Module 1.1.7 Key Libraries for image processing in Python 03:58
    • 9: 09 Module 1.1.8 Basic Image Operation 04:16
    • 10: 10 Module 1.1.8 Continuation of Basic Image Operation 05:44
    • 11: 11 Module 1.1.8 Continuation of Basic Image Operation 06:17
    • 12: 12 Module 2.1.1 Image Representation and Feature Extraction 03:54
    • 13: 13 Module 2.1.1 Continuation of image Representation and Feature Extraction 05:36
    • 14: 14 Module 2.1.2 Corner Detection 07:27
    • 15: 15 Module 2.1.3 HOG(Histogram of Oriented Gradients) 07:34
    • 16: 16 Module 3.1.1 Image Segmentation 03:27
    • 17: 17 Module 3.1.2 Types of image Segmentation 04:18
    • 18: 18 Module 3.1.3 Technique and Implementations 03:50
    • 19: 19 Module 3.1.4 K-Means Clustering 04:31
    • 20: 20 Module 3.1.5 Watershed Algorithm 08:21
    • 21: 21 Module 3.1.6 Summary 03:27
    • 22: 22 Module 4.1.1 Object Detection 03:26
    • 23: 23 Module 4.1.2 Key Concepts in Object Detection 08:57
    • 24: 24 Module 4.1.3 Implementing Object Detection with Pre trained Models 05:27
    • 25: 25 Module 4.1.4 YOLO(You only Look Once) 08:15
    • 26: 26 Module 4.1.5 Faster R-CNN with TensorFlow 08:04
    • 27: 27 Module 4.1.6 Summary 04:07
    • 28: 28 Module 5.1.1 Image Classification 03:44
    • 29: 29 Module 5.1.2 Key Components in image Classification 06:38
    • 30: 30 Module 5.1.3 Implementing image Classification 08:12
    • 31: 31 Module 5.1.4 Deep learning Methods 07:53
    • 32: 32 Module 6.1.1 Image Recognition and Scene Understanding 03:49
    • 33: 33 Module 6.1.2 Key Concepts 11:25
    • 34: 34 Module 6.1.3 Implementations 04:45
    • 35: 35 Module 6.1.4 Scene Understanding with Semantic Segmentation 05:22
    • 36: 36 Module 6.1.5 Instance Segmentation with Mask R-CNN 06:26
    • 37: 37 Module 6.1.6 Scene Classification with RNN and CNN 07:12
    • 38: 38 Module 6.1.6 Continuation of Scene Classification with RNN and CNN 05:39
    • 39: 39 Module 7.1.1 Object Tracking 05:41
    • 40: 40 Module 7.1.2 Key Concepts 09:35
    • 41: 41 Module 7.1.3 KLT Tracker with OpenCV 08:16
    • 42: 42 Module 7.1.4 Deep SORT with YAOLOv4 for Detection 11:39
    • 43: 43 Module 8.1.1 Image Generation and image to Image Translation 05:12
    • 44: 44 Module 8.1.2 key concepts 08:34
    • 45: 45 Module 8.1.3 Implementations 09:09
    • 46: 46 Module 8.1.4 Image to Image Translation with Pix2Pix 12:25
    • 47: 47 Module 8.1.5 Cycle gan for Unpaired Image to Image Translation 10:53
    • 48: 48 Module 8.1.5 Continuation of Cycle gan for Unpaired Image to Image Translatio 05:56
    • 49: 49 Module 9.1.1 Advanced Topics in Computer Vision 09:31
    • 50: 50 Module 9.1.1 Continuation of Advanced Topics in Computer Vision 04:36
    • 51: 51 Module 9.1.1 Continuation of Advanced Topics in Computer Vision 12:37
    • 52: 52 Module 10.1.1 Computer Vision Applications and Future Trends 06:14
    • 53: 53 Module 10.1.2 Application 06:10
    • 54: 54 Module 10.1.3 Future Trends 06:16
    • 55: 55 Module 10.1.3 Continuation of Future Trends 03:55
    • 56: 56 Module 11.1.1 Capstone Project 04:24
    • 57: 57 Module 11.1.2 Project Title Real-world Object Detection and Classification Sy 03:58
    • 58: 58 Module 11.1.3 Project Tasks 05:45
    • 59: 59 Module 11.1.3 Continuation of project Tasks 04:22
    • 60: 60 Module 11.1.4 Project Deliverables 07:42
    • 61: 61 Module 11.1.5 Project Evaluation 03:32
    • 62: 62 Module 11.1.6 Conclusion 03:29
    • 63: 63 Assignment 00:36
    • 64: Leave a Review 01:00
    • 65: CPD Certificate 01:00

Course media

Description

Course Curriculum

  • Module 01: Introduction and study plan
  • Module 02: Overview of Computer Vision
  • Module 03: Key Components of Computer Vision
  • Module 04: Pattern Recognition
  • Module 05: Technique and Algorithms
  • Module 06: Challenges in Computer Vision
  • Module 07: Basic of Image Processing with Python
  • Module 08: Key Libraries for image processing in Python
  • Module 09: Basic Image Operation
  • Module 10: Continuation of Basic Image Operation
  • Module 11: Continuation of Basic Image Operation
  • Module 12: Image Representation and Feature Extraction
  • Module 13: Continuation of image Representation and Feature Extraction
  • Module 14: Corner Detection
  • Module 15: HOG(Histogram of Oriented Gradients)
  • Module 16: Image Segmentation
  • Module 17: Types of image Segmentation
  • Module 18: Technique and Implementations
  • Module 19: K-Means Clustering
  • Module 20: Watershed Algorithm
  • Module 21: Summary
  • Module 22: Object Detection
  • Module 23: Key Concepts in Object Detection
  • Module 24: Implementing Object Detection with Pre trained Models
  • Module 25: YOLO(You only Look Once)
  • Module 26: Faster R-CNN with TensorFlow
  • Module 27: Summary
  • Module 28: Image Classification
  • Module 29: Key Components in image Classification
  • Module 30: Implementing image Classification
  • Module 31: Deep learning Methods
  • Module 32: Image Recognition and Scene Understanding
  • Module 33: Key Concepts
  • Module 34: Implementations
  • Module 35: Scene Understanding with Semantic Segmentation
  • Module 36: Instance Segmentation with Mask R-CNN
  • Module 37: Scene Classification with RNN and CNN
  • Module 38: Continuation of Scene Classification with RNN and CNN
  • Module 39: Object Tracking
  • Module 40: Key Concepts
  • Module 41: KLT Tracker with OpenCV
  • Module 42: Deep SORT with YAOLOv4 for Detection
  • Module 43: Image Generation and image to Image Translation
  • Module 44: key concepts
  • Module 45: Implementations
  • Module 46: Image to Image Translation with Pix2Pix
  • Module 47: Cycle gan for Unpaired Image to Image Translation
  • Module 48: Continuation of Cycle gan for Unpaired Image to Image Translation
  • Module 49: Advanced Topics in Computer Vision
  • Module 50: Continuation of Advanced Topics in Computer Vision
  • Module 51: Continuation of Advanced Topics in Computer Vision
  • Module 52: Computer Vision Applications and Future Trends
  • Module 53: Application
  • Module 54: Future Trends
  • Module 55: Continuation of Future Trends
  • Module 56: Capstone Project
  • Module 57: Project Title Real-world Object Detection and Classification System
  • Module 58: Project Tasks
  • Module 59: Continuation of project Tasks
  • Module 60: Project Deliverables
  • Module 61: Project Evaluation
  • Module 62: Conclusion
  • Module 63: Assignment

Certification

Once you’ve successfully completed your Computer Vision Course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £5.99). Our Computer Vision Course certification has no expiry dates, although we do recommend that you renew them every 12 months.

Who is this course for?

  • People interested in learning computer vision with Python.
  • Beginners wanting to explore image and object detection.
  • Students aiming to understand deep learning vision methods.
  • Anyone curious about AI scene and image classification.
  • Learners wanting to build vision-based projects.

Career path

  • Computer Vision Technician
  • AI Vision Algorithm Developer
  • Image Processing Assistant
  • Object Detection System Designer
  • Image Classification Analyst
  • Scene Understanding Research Assistant

Questions and answers

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Reviews

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Legal information

This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
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