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Computer Vision for Beginners with Python
Xcel Learning

Complimentary Assessment | Digital Certificate | 24/7 Support | Lifetime Access | Transparent Pricing

Summary

Price
£19 inc VAT
Study method
Online, On Demand 
Duration
1.4 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed Courses Certificate of Completion - Free
Assessment details
  • Review Questions and Assessments (included in price)
Additional info
  • Tutor is available to students

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Overview

Computer Vision for Beginners with Python course provides a comprehensive introduction to computer vision using Python, designed specifically for beginners with little to no prior experience. You will start by understanding how images are represented digitally and gradually progress to practical image processing techniques using industry-standard tools like OpenCV and NumPy. As the course advances, you will explore image transformations, feature detection, video processing, and real-time computer vision applications. You will also gain hands-on exposure to modern deep learning concepts, including convolutional neural networks, transfer learning, and object detection. By combining theory with practical projects, this course helps you build strong foundational skills while preparing you for real-world applications. Whether you aim to build AI-powered apps, pursue research, or enter the field of artificial intelligence, this course equips you with the knowledge and confidence to start your computer vision journey.

Certificates

Assessment details

Review Questions and Assessments

Included in course price

Curriculum

13
sections
13
lectures
1h 23m
total

Description

Discover the Exciting Subjects Awaited in This Course!

Chapter 1: Introduction to Computer Vision

  1. What is Computer Vision?
  2. Real-World Applications of Computer Vision
  3. Overview of Python Ecosystem for Vision
  4. Setting Up the Development Environment
  5. Course Roadmap and Learning Strategy

Chapter 2: Python Fundamentals for Vision

  1. Essential Python Syntax Refresher
  2. Working with NumPy Arrays
  3. Introduction to Matplotlib for Visualization
  4. File Handling for Images and Videos
  5. Writing Clean and Reusable Code

Chapter 3: Working with Images

  1. Understanding Digital Images (Pixels, Channels)
  2. Reading and Displaying Images with OpenCV
  3. Image Color Spaces (RGB, BGR, Grayscale, HSV)
  4. Saving and Exporting Images
  5. Basic Image Metadata Handling

Chapter 4: Image Transformations

  1. Resizing and Cropping Images
  2. Image Rotation and Flipping
  3. Translation and Affine Transformations
  4. Perspective Transformations
  5. Practical Transformation Use Cases

Chapter 5: Image Processing Fundamentals

  1. Image Blurring and Smoothing
  2. Thresholding Techniques
  3. Morphological Operations
  4. Edge Detection (Canny, Sobel)
  5. Image Gradients Explained

Chapter 6: Drawing and Annotation

  1. Drawing Shapes with OpenCV
  2. Writing Text on Images
  3. Creating Custom Overlays
  4. Interactive Mouse Events
  5. Building Annotation Tools

Chapter 7: Working with Video

  1. Capturing Video from Webcam
  2. Reading and Writing Video Files
  3. Frame-by-Frame Processing
  4. Real-Time Display and Performance Tips
  5. Video Processing Mini Project

Chapter 8: Feature Detection and Matching

  1. Corner Detection (Harris, Shi-Tomasi)
  2. SIFT, SURF, and ORB Features
  3. Feature Descriptors Explained
  4. Feature Matching Techniques
  5. Panorama Stitching Basics

Chapter 9: Object Detection Basics

  1. Introduction to Object Detection
  2. Haar Cascades in OpenCV
  3. Face Detection Project
  4. Eye and Smile Detection
  5. Limitations of Classical Methods

Chapter 10: Introduction to Deep Learning for Vision

  1. From Traditional CV to Deep Learning
  2. Basics of Neural Networks
  3. Convolutional Neural Networks (CNNs)
  4. Popular Deep Learning Frameworks
  5. Setting Up a Deep Learning Environment

Chapter 11: Deep Learning Projects

  1. Image Classification with CNNs
  2. Transfer Learning with Pretrained Models
  3. Object Detection with YOLO Basics
  4. Image Segmentation Overview
  5. Evaluating Model Performance

Chapter 12: Building Real-World Applications

  1. Deploying Models Locally
  2. Building a Simple Vision App
  3. Performance Optimization Tips
  4. Packaging and Sharing Projects
  5. Next Steps and Advanced Learning Paths

Don't miss out on the chance to discover your full potential. Enroll today and open the door to a world of opportunities. Receive an exclusive digital certificate upon completing the course!

Who is this course for?

This course is designed for aspiring programmers, data enthusiasts, and students who want to explore computer vision using Python. It suits beginners with coding knowledge, professionals transitioning into AI, and hobbyists interested in image processing, object detection, and real-world applications without requiring prior experience in advanced mathematics or machine learning.

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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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