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Course Details
Course Details
What You'll Learn
Topic 1: Evolution of Business AI from Generative AI to Autonomous AI Agents
Topic 2: Generative AI, Agentic AI and AI Agent Technologies
Topic 3: Context, Harness and Loop Engineering for AI Agents
Topic 4: Business Opportunities and Innovative Agentic AI Use Cases
Topic 5: Evaluating and Implementing AI-Driven Business Innovation
Course Info
Prerequisite
Participants should already understand basic reading, computer use and familiarity with the workplace topic covered by this course. Review the fundamentals before attending this course.
Software Requirement
- A PDF reader and a modern web browser for course materials.
No specialist application installation is required unless specified in your joining instructions. Prepare access to the course materials before class.
Hardware Requirement
Bring a laptop or tablet capable of reading PDF course materials and completing written 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
- AI Specialist
- Machine Learning Engineer
- Data Scientist
- AI Solutions Architect
- AI Consultant
- AI Researcher
- Data Engineer
- Business Intelligence Analyst
- AI Project Manager
- AI Product Manager
- Automation Engineer
- IT Specialist (AI)
- Software Engineer (AI/ML)
- Cloud AI Engineer
- AI Ethics & Compliance Specialist
- AI Trainer & Educator
- Digital Transformation Manager
- AI Application Developer
- Big Data Analyst
- AI Policy Advisor
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.
Preparing for a vendor-neutral AI certification is easier with a trainer who both builds AI systems and builds exam-preparation tools, and Dr. Alfred Ang does both. 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 in Singapore, the training organisation behind Tertiary Courses. The work spans front end, back end, databases, cloud deployment and native mobile apps, and more than 300 public repositories on GitHub record it. On the exam side, Dr. Ang built a full-stack certification practice-exam platform with timed exam and practice modes and AI-assisted question generation, together with native iOS and Android apps for practising on the move. On the AI side, the portfolio touches each stage the exam examines. Interactive Python and Streamlit labs teach data mining and machine learning from first principles. An inventory forecasting agent runs on a local language model, a deliberate choice that keeps business data in-house. Several native apps perform on-device AI, such as text recognition and background removal, so personal images never leave the phone. An HR chatbot on n8n is wrapped in input and output guardrails to prevent misuse, and an AI interview simulator scores candidates against role-specific rubrics, raising real questions of fairness and transparency. Dr. Ang has also hardened websites with strict content security policies and published a tool that scans code for leaked secrets, practical experience of the security planning the exam expects candidates to understand.
This course works systematically through the official INF-307 exam domains, connecting each objective to a concrete example. The AI problem definition domain covers identifying whether AI is warranted, defining inputs, outputs and measurable success, classifying problems as classification, regression, unsupervised or reinforcement learning, identifying the expertise required, building a security plan, guarding against misprediction and harm, and choosing transparency and validation activities. The data domain covers choosing collection methods, assessing quality, representativeness and bias, estimating compute, time and budget, converting data and engineering features, separating training and test sets, and documenting data decisions for regulators and end users. The algorithms and models domain evaluates families such as neural networks, decision trees and k-means, then trains, tunes and measures models iteratively, before the course moves on to the remaining domains on model selection and responsible deployment. Dr. Ang explains each objective in plain language, illustrates it with a real design decision, such as why a forecasting agent runs locally or why a chatbot needs output guardrails, and then tests understanding with exam-style questions whose wrong answers are discussed as carefully as the right ones. The style is structured, supportive and candid about which topics most often catch candidates out, and learners are encouraged to bring questions from their own workplace to test the concepts against real situations. Learners leave with a firm grasp of the full AI life cycle and a focused revision plan. To see the practice-exam platform and AI projects that inform this course, visit Dr. Ang's portfolio at https://github.com/alfredang
Review
Customer Reviews (6)
- Very satisfied 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 3/16/2026)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 - Fantastic experience Review by Course Participant/Trainee
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The training was very practical and hands-on. I could apply what I learned to my work immediately. (Posted on 6/12/2025)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 - Superb training 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 4/4/2025)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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Solid content and a supportive trainer. I would definitely sign up for more courses here. (Posted on 1/26/2025)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 - Engaging trainer Review by Course Participant/Trainee
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Well organised course with clear objectives. The step-by-step approach made it easy to follow. (Posted on 6/25/2024)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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