Business Innovation with AI

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