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Course Details
Course Details
What You'll Learn
Topic 1: Overview of Computer Vision with Deep Learning
Topic 2: Basic Computer Vision Techniques
Topic 3: Image Classification
Topic 4: Object Detection
Topic 5: Computer Vision Systems
Course Info
Minimum Entry Requirement
Knowledge and Skills
- Able to operate using computer functions
- Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
- Positive Learning Attitude
- Enthusiastic Learner
Experience
- Minimum of 1 year of working experience.
Target Age Group: 18-65 years old
Minimum Software/Hardware Requirement
You will need the following for this course:
- Python 3 – free
- Visual Studio Code – free
- Google Colab – free, runs in the browser with a Google account
- An AI coding assistant with a free tier, e.g. GitHub Copilot Free in Visual Studio Code
Install and test the listed software before class. Confirm the required version and account or licence access in your joining instructions. Cloud and AI services may charge subscription or usage fees.
Bring a Windows or Mac laptop supported by the listed software, with a working keyboard and sufficient storage for course exercises.
Bring your own computer and charger, with a reliable internet connection and permission to install the required software. For live online classes, also prepare a microphone or headset.
If you need help arranging a suitable device, contact us before the class to confirm availability.
Job Roles
Job Roles
- Computer Vision Engineer
- Machine Learning Engineer
- Robotics Developer
- Augmented Reality Developer
- Video Analytics Specialist
- Multimedia Developer
- Image Processing Engineer
- Visual Effects Artist
- Software Developer (with a focus on visual applications)
- Drone Technology Developer
- Biometrics Systems Engineer
- Autonomous Vehicle Systems Developer
- Medical Imaging Specialist
- CCTV and Surveillance Systems Designer
- Game Developer.
Trainers
Trainers
Dr. Muhammed Siraj is a cybersecurity specialist and academic with 15 years of experience, serving as Senior Lecturer in the Department of Informatics and Director of Graduate and Continuous Education at Academic City University in Accra. He holds a PhD in Security in Computing from Universiti Putra Malaysia, where his research focused on blockchain security, an MSc in Information and Communication Technology from Aalborg University in Denmark, and a degree from the University of Ghana. He is a licensed independent assessor with Ghana's Cyber Security Authority, holds leadership roles in national cybersecurity and privacy professional bodies, and is certified as a Certified Ethical Hacker (CEH), Computer Hacking Forensic Investigator (CHFI) and Certified Data Protection Supervisor (CDPS). Before joining Academic City, he served as Acting Director of ICT and Data Protection Supervisor at Wisconsin International University College. His expertise spans penetration testing, digital forensics, incident response, malware analysis, cryptography, cloud and IoT security, zero trust architecture, cyber threat intelligence, data protection, and governance, risk and compliance.
Dr. Siraj's research interests cover blockchain, cybersecurity, distributed computing, consensus protocols and machine learning, with published work on blockchain consensus security, smart-contract privacy preservation for sensitive records in the cloud, and new consensus mechanisms. This background gives him a distinctive perspective on artificial intelligence: as organisations adopt AI tools, agents and data-driven automation, he helps learners understand how to protect the data these systems depend on, recognise new attack surfaces, and put sound governance, privacy and access controls in place so that AI can be used safely and responsibly. In the classroom he connects technical practice with regulation, privacy and human factors, drawing on real-world security and data-protection cases to show how concepts apply in professional settings, and his experiential, hands-on teaching style has made him a trusted trainer for students and working professionals alike.
Dr. Alfred Ang brings a builder's perspective to computer vision, having shipped image-understanding features in real apps used on phones every day. Dr. Ang is a full-stack AI developer, Agentic AI solution architect and corporate trainer, and serves as CTO of Tertiary Infotech Academy Pte Ltd, the Singapore training organisation behind Tertiary Courses, delivering solutions end-to-end across front end, back end, databases, cloud deployment and native mobile apps. More than 300 public repositories on GitHub document that work. Dr. Ang built a native iOS document scanner that captures, enhances and runs on-device OCR fully offline, and an Android version that uses ML Kit for edge detection and text recognition before exporting to PDF. A passport photo app removes backgrounds on the device and crops faces to official requirements, while a digital business card app performs single-shot card scanning with live edge detection and Vision OCR, then parses the details with Apple Intelligence. Dr. Ang also created interactive Python and Streamlit labs for beginner data mining and machine learning, and uses AI coding assistants extensively across the portfolio. Together these projects cover the full path from a camera frame to a useful result, which is exactly the journey learners follow in this course, only with AI assistants writing much of the code. Shipping these features to real users has taught Dr. Ang how quickly computer vision meets messy reality, including shadows, blur, unusual angles and cluttered backgrounds, and how important it is to test widely and design graceful fallbacks when a model is unsure.
AI Vibe Coding for Computer Vision lets participants build working computer vision applications in Python by directing AI coding assistants such as Cursor, GitHub Copilot and Claude. Dr. Ang opens with an overview of computer vision with deep learning, explaining how models learn from images and where vision delivers business value, from quality inspection and people counting to document scanning and retail analytics. Learners then use vibe coding to implement basic computer vision techniques, including reading images and video, filtering, edge detection and simple transformations, while reviewing each piece of generated code so they understand what it does. The third and fourth topics cover image classification and object detection, where participants prompt the assistant to load pretrained models, prepare data, run inference and interpret results. The final topic brings everything together into complete computer vision systems that take input from a camera or folder and produce useful output. Because Dr. Ang has already moved vision features from prototype to published app, the class stresses practical details such as testing on real images, handling poor lighting and checking accuracy before trusting a model. Teaching is hands-on and step-by-step, and learners are encouraged to apply each technique to a use case from their own work. Participants also consider deployment choices, such as running models on a laptop, a server or a mobile device, and the privacy implications of processing images that may contain people or personal documents, so that their solutions are not only accurate but also responsible and practical. Browse Dr. Ang's vision and mobile projects at https://github.com/alfredang.
Review
Customer Reviews (9)
- Learned a lot Review by Course Participant/Trainee
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The course content was comprehensive and up to date. The practical exercises were the best part. (Posted on 4/28/2026)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Great practical training Review by Course Participant/Trainee
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Really enjoyed the training. The examples were relevant and the pace was just right. (Posted on 2/12/2026)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Clear and easy to follow Review by Course Participant/Trainee
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Really enjoyed the training. The examples were relevant and the pace was just right. (Posted on 8/6/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Clear and easy to follow Review by Course Participant/Trainee
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Good hands-on training with plenty of examples. I feel much more confident applying these skills now. (Posted on 6/26/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Hands-on and practical Review by Course Participant/Trainee
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Great course materials and well-paced lessons. The exercises really helped me understand the topic. (Posted on 4/16/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
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