Every service here was built from a real classroom need — not a marketing brief. From virtual classes to AI-powered exam tools, we cover the full learning journey.
Six services. One mission: make quality STEM education accessible and effective for Nigerian secondary school students.
Live, structured online sessions for JSS 1 – SSS 3 students. Each class follows a clear plan: theory, worked examples, class exercises, and Q&A. No shortcuts.
A free, fully-featured Computer-Based Testing system — built entirely by HMG Academy. Students take timed exams, get auto-marked results, and receive AI-generated explanations per question.
A structured, portfolio-first programme for beginners who want to break into data science — built by someone actively doing it. Python, ML, and real projects from day one.
Personalised sessions for students who need focused, individual attention. We diagnose gaps, work through them systematically, and track progress at each session.
Downloadable, WAEC/NECO-aligned notes across all core subjects. Written by the same teacher who delivers the classes — so the explanations match the way you'll be taught.
Through HMG Concepts, we assist students and families with WAEC, NECO, and JAMB registration — handling forms, deadlines, and documentation so nothing falls through the cracks.
I built CBT Pro because I was tired of watching students prepare well but freeze on exam day because they had never sat a proper computer-based test. Now they can practise — for free — on the exact format WAEC and NECO use.
It was built on an Android tablet using Acode editor. That's not a limitation — that's a proof of concept. If you can think clearly, the tools don't matter.
Most data science courses teach you tools. This programme teaches you thinking. Because a data scientist who can't reason about a problem clearly is just someone who knows how to run code.
I'm not a trainer who has never worked in the field. I am actively building ML models, shipping portfolio projects, and documenting the real journey — including the failures. That's what you'll learn from.
Variables, data types, loops, functions, and the Pandas/NumPy stack. We start from the very beginning — no assumptions.
Loading, cleaning, transforming, and visualising data. Non-graphical analysis always before graphical — the right workflow from day one.
Classification, regression, and ensemble methods using Scikit-learn. Feature engineering, model selection, anti-leakage practices, and proper evaluation.
SHAP values, model interpretation, Streamlit dashboards, and publishing your portfolio on GitHub. Your work becomes public proof.