Course Catalogue
Three Paths, One Direction
Each programme occupies a distinct place in the curriculum. Review the details below and send an enquiry when you have a sense of which fits your situation.
Back to HomeOur Methodology
How We Teach
Concept Before Code
We establish why a technique works before showing how to implement it. Learners who understand the reasoning are better placed to adapt when conditions change.
Iterative with Feedback
Work is reviewed, returned with comments, and can be revised. Each cycle improves the learner's understanding more than a single pass.
Professional Framing
Every course places technical content inside a professional context. The question is not just "what does this do" but "when would you use this and what are the costs of getting it wrong".
Station 01 — Foundations
Mathematics for Practical AI
A five-week course that revisits the mathematics actually used in AI work. The emphasis is on linear algebra, probability, and simple optimisation — not as abstract theory but as tools that appear regularly in models, papers, and toolchain documentation. Each week's material is tied to a practical setting so the connection between the mathematics and working code is always visible.
Weekly problem sets are returned with written feedback. Learners who want to revise their work may do so within the course window. The course is well suited to anyone who covered these topics during a degree but has not used them in the years since.
What the Course Covers
Vectors, matrices, and linear transformations
Why matrix multiplication is everywhere in AI and what it is actually doing
Probability and conditional reasoning
Bayesian thinking, distributions, and why this matters for model outputs
Gradient descent and simple optimisation
What a loss function is, how models learn, and what can go wrong
From mathematics to Python
NumPy and related libraries as implementations of the concepts covered
Reading technical documentation
Applying the course vocabulary to actual model papers and API references
Key Benefits
- Confident interpretation of model documentation
- Practical bridge between theory and NumPy/Pandas usage
- Stronger foundation for the Applied AI Systems Programme
- Written feedback on every problem set submitted
- Letter of completion on course finish
Key Benefits
- End-to-end AI system built as a portfolio artefact
- Weekly written feedback from a named practitioner mentor
- Experience coordinating AI work in a team setting
- Documentation standards that transfer to workplace projects
- Instalment payment option available
Station 02 — Applied Work
Applied AI Systems Programme
A sixteen-week programme in which small teams design, build, and document an applied AI system from first principles to deployment. The scope covers the full decision chain — what problem to scope, how to think about data strategy, which modelling approach fits, how to evaluate, and what deployment involves. No stage is treated as someone else's responsibility.
Each team is paired with a mentor who reviews progress weekly and provides written feedback on both technical and process decisions. The programme is deliberately paced for working professionals; the weekly commitment is demanding but manageable alongside employment.
Programme Phases
Scoping and problem definition (Weeks 1–3)
Framing the problem correctly and identifying what data would help
Data strategy and preparation (Weeks 4–7)
Sourcing, cleaning, and organising data; understanding its limitations
Model selection and development (Weeks 8–11)
Choosing appropriate approaches and building iteratively
Evaluation and documentation (Weeks 12–14)
Assessing what the system does well and where it falls short
Deployment considerations and final review (Weeks 15–16)
What is involved in serving a model and presenting the completed system
Station 03 — Orientation
Evening Talk Series on AI in the Workplace
A four-session evening series for professionals who want to understand how AI is being adopted in Malaysian workplaces without yet committing to a longer programme. Each session takes one industry — finance, health, education, and the public sector — and examines realistic use cases alongside limitations and governance questions.
The series brings in a practitioner from the relevant sector for each session. Attendance is structured around discussion rather than lecture. Participants leave with a curated reading list and a written record of the discussion covered.
Session Breakdown
AI in Financial Services
Credit scoring, fraud detection, and regulatory considerations in Malaysian banking
AI in Healthcare
Diagnostic support, patient data, and the governance questions specific to clinical settings
AI in Education
Personalised learning tools, assessment, and the limits of automation in educational contexts
AI in the Public Sector
Service delivery, policy data, procurement considerations, and accountability structures
Key Benefits
- Sector-specific speakers, not generalist overviews
- Discussion record provided after each session
- Reading list for each of the four industries
- Low time commitment — evenings only
- Can be taken alongside either of the two main courses
Decision Guide
Choosing Between the Programmes
If you are unsure which course fits your situation, this table may help. You are also welcome to send an enquiry and we will advise directly.
| Feature | Mathematics | Applied Systems | Evening Series |
|---|---|---|---|
| Duration | 5 weeks | 16 weeks | 4 sessions |
| Price (RM) | 760 | 3,520 | 140 |
| Written feedback | |||
| Team-based work | |||
| Letter of completion | |||
| Hands-on coding | |||
| Best for | Refreshing maths foundations | Building a complete AI system | Understanding AI adoption |
Professional Standards
Shared Across All Programmes
Data Privacy
Learner data is held in accordance with PDPA 2010. It is not shared with third parties or used for purposes outside enrolment and course administration.
Clear Course Outlines
A full outline, including assessment schedule, is provided before enrolment is confirmed. Learners know what is expected before they commit.
Accurate Expectations
Course descriptions reflect what is taught, not what sounds appealing. We do not overstate the speed or ease of learning AI development.
Course Fees
Pricing
Station 01
Mathematics for Practical AI
RM 760
Per learner, one-time payment
- 5 weeks, all materials included
- Written feedback on problem sets
- Letter of completion
Station 02
Applied AI Systems Programme
RM 3,520
Two instalments available
- 16 weeks, all materials included
- Dedicated practitioner mentor
- End-to-end AI system portfolio piece
- Letter of completion
Station 03
Evening Talk Series
RM 140
Per participant, all four sessions
- 4 evening sessions
- Discussion records included
- Reading lists per industry
Begin the Enquiry
Not Sure Which Course to Start With?
Tell us a little about your background and current role. We will suggest the most appropriate starting point and answer any questions before you commit to anything.
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