01
What is Computer Vision
Understand how machines interpret visual data
Courses Introduction to Computer Vision
Course Course
Humain Academy
Introduction to Computer Vision
Go from understanding images to building AI-powered image recognition systems in 6 weeks.
- Duration
- 45 total learning hours over 6 weeks
- Level
- Intermediate - Basic knowledge of Python programming and familiarity with Machine Learning concepts is recommended
- Format
- 12 live online lectures + Q&A clinics + self-study + assessments
- Delivery
- Online only, live video conferencing
About the Course
Introduction to Computer Vision
This course introduces you to Computer Vision, the field of AI that enables machines to interpret and understand visual data from images and video.
Through hands-on sessions and practical exercises, you will learn how images are processed, how features are extracted, and how machine learning and deep learning models are used to recognise and classify visual information.
By the end of the course, you will be able to build your own image recognition and object detection applications using industry-standard tools and frameworks.
Prerequisites
Who is this course for?
Open to all individuals aged 16+ with a basic knowledge of Python programming and familiarity with Machine Learning concepts
AI & Data Enthusiasts
Learners interested in visual AI applications
Python & ML Learners
Those with basic knowledge looking to specialise further
Aspiring Computer Vision Engineers
Individuals exploring careers in AI and automation
Learning Outcomes
Skills you’ll demonstrate
02
Image Structure
Learn how pixels, colour spaces, and formats work
03
Filtering & Edge Detection
Enhance and analyse images
04
Transformations & Augmentation
Prepare images for machine learning
05
Feature Extraction Methods
Use techniques like HOG, SIFT, and ORB
06
Image Representation
Convert images into meaningful data for models
07
Image Classification
Build models to recognise objects in images
08
Model Evaluation
Measure performance and improve accuracy
09
CNN Architecture
Understand how deep learning models process images
10
Training & Tuning
Optimise models for better performance
11
Object Detection
Use techniques like YOLO and bounding boxes
12
Transfer Learning
Apply pre-trained models to real-world problems
13
Tools & Frameworks
Work with OpenCV and TensorFlow/Keras
14
Final Project
Build a complete image recognition application
Curriculum Modules
1 Session 1 What is computer vision?
Explore how machines interpret images and real-world applications
2 Session 2 How images work
Understand pixels, colour spaces, and image formats
3 Session 3 Image processing (Part 1)
Apply filters and edge detection techniques
4 Session 4 Image processing (Part 2)
Perform transformations and data augmentation
5 Session 5 Feature detection
Extract key features using HOG, SIFT, and ORB
6 Session 6 Image classification
Build models to classify images using ML algorithms
7 Session 7 CNNs (Part 1)
Understand convolutional neural network architecture
8 Session 8 CNNs (Part 2)
Train, tune, and evaluate CNN models
9 Session 9 Object detection
Detect objects using YOLO and bounding box techniques
10 Session 10 Transfer learning
Use pre-trained models for faster development
11 Session 11 OpenCV & TensorFlow
Work with industry-standard computer vision tools
12 Session 12 Final project
Build an image recognition or detection application
FAQs
Do I need prior experience in Computer Vision?
No, this course starts from the fundamentals. However, basic Python and some understanding of Machine Learning will help you get the most out of it.
Will I build real applications?
Yes. You will complete hands-on exercises throughout the course and build a final image recognition or object detection application.
What tools and technologies will I use?
You’ll work with Python using popular libraries such as OpenCV and TensorFlow/Keras.
Is this course suitable for beginners?
This is best suited for learners with some prior experience in Python and Machine Learning who want to specialise in Computer Vision.
Build AI That Can See the World
Enroll now or request information about upcoming sessions.
Introduction to Computer Vision
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