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Course Details
Course Details
What You'll Learn
Topic 1: AI Vibe Coding and Deep Learning Environment Setup
Topic 2: Neural Networks for Regression and Predictive Modelling
Topic 3: Neural Networks for Data Classification
Topic 4: Convolutional Neural Networks for Image Classification
Topic 5: Transfer Learning and Fine-Tuning Pre-Trained Model
Course Info
Prerequisite
Basic computer and file-management skills are expected. Familiarity with basic programming, data handling and elementary statistics will help you follow the exercises.
Software Requirement
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.
Also prepare the tools needed for the practical exercises:
Confirm the version, account permissions and any API usage budget with the trainer before class.
Hardware Requirement
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
- Machine Learning Engineer
- Data Scientist
- Deep Learning Researcher
- AI Developer
- Neural Network Designer
- Computer Vision Engineer
- NLP Engineer (branching into deep learning)
- AI Product Manager (technical understanding)
- Robotics Engineer (with AI components)
- Bioinformatics Scientist (deep learning applications)
- Medical Imaging Specialist (AI-focused)
- Game Developer (AI-driven features)
- Predictive Analytics Specialist
- AI/ML Educator or Trainer
- Autonomous Systems 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.
Deep learning powers many of the features that Dr. Alfred Ang ships in production, from image processing on mobile devices to voice and avatar generation. 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. Several of Dr. Ang's native iOS and Android apps rely on on-device neural networks: document scanners that detect page edges and run OCR with ML Kit and Apple Vision, a passport-photo app that removes backgrounds and crops around detected faces, and a business-card scanner that parses contact details with Apple Intelligence. Dr. Ang has also built lipsyncdemo, which turns a portrait and a script into a talking avatar using engines such as MuseTalk, and digitalhuman, a voice-cloned avatar service trained on custom content. The data-mining-ml-labs repository provides interactive Streamlit labs for machine learning, and a GPU-accelerated fractal generator explores the mathematics behind rendering on the graphics card. More than 300 public repositories at github.com/alfredang were largely written with AI coding assistants in a vibe coding style, which gives Dr. Ang a realistic view of how AI can accelerate neural-network development while still demanding a sound grasp of architectures, training behaviour and evaluation. Dr. Ang has learned that practical deep learning depends as much on good data, sensible baselines and patient experimentation as on clever architectures.
AI Vibe Coding for Deep Learning brings that perspective to a structured, hands-on programme. The first topic sets up AI vibe coding and the deep learning environment, configuring Python, notebooks and frameworks such as TensorFlow, Keras or PyTorch with the help of an AI assistant. Learners then build neural networks for regression and predictive modelling, followed by networks for data classification, learning how layers, activation functions, loss functions and optimisers work together. The fourth topic introduces convolutional neural networks for image classification, the same family of models behind the scanning and photo features in Dr. Ang's mobile apps. The final topic covers transfer learning and fine-tuning pre-trained models, showing how to adapt a powerful existing model to a small, specialised dataset. Dr. Ang prompts the AI live, then stops to explain the generated code, inspect training curves and diagnose problems such as over-fitting or poor data quality. Participants practise every step themselves and are encouraged to bring a simple dataset or image collection from their own work. The emphasis throughout is on understanding rather than copying, so learners can judge whether a model is genuinely good enough to use. Engineers, analysts and technically minded professionals will leave with working models and a repeatable workflow. Learners also discuss deployment options, from cloud notebooks to lightweight on-device models, so they understand how a trained network can move from experiment to a real application. Guidance on computing resources and free cloud GPUs is included as well. To explore the on-device AI and deep-learning projects behind this course, visit Dr. Ang's portfolio at https://github.com/alfredang.
Review
Customer Reviews (5)
- Hands-on and practical Review by Course Participant/Trainee
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The trainer is an expert in the subject and shared many practical insights from real projects. (Posted on 6/20/2026)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 - Very useful course Review by Course Participant/Trainee
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Solid content and a supportive trainer. I would definitely sign up for more courses here. (Posted on 1/17/2026)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 learning experience 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 3/12/2025)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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Very informative and interactive. The small class size meant we got a lot of personal attention. (Posted on 10/22/2024)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 - Excellent course Review by Course Participant/Trainee
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A very practical course. The trainer made a complex topic simple and enjoyable to learn. (Posted on 1/5/2024)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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