Three Programmes.
Defined Scope. Clear Output.
Each programme covers a specific part of the AI development skill set. Choose based on where you are now, not where you hope to be after a vague overview course.
Back to HomeHow the Programmes Work
Defined Scope per Programme
Each programme has a published syllabus with specific topics, deliverables and time commitment. What you're signing up for is described before you sign up.
Work Reviewed, Not Just Submitted
Assignments and projects are reviewed by practitioners, not auto-graders. Feedback is specific to what you built, not generic comments about a category of error.
Output Artefacts Documented
From notation sheets to portfolio assessments, every programme produces something that exists after the programme ends and that can be shown to someone who wasn't part of the cohort.
Mathematics for Machine Learning Refresher
A seven-week refresher for people who can code but find the notation in papers slows them down. It covers linear algebra as it is actually used in modelling, calculus for gradients, probability and distributions, expectation and variance intuition, and enough optimisation theory to understand why training behaves the way it does. Every concept is paired with a short notebook that makes it concrete. Suitable for developers returning to mathematics after some years away.
- Fourteen live sessions scheduled for MYT
- Weekly problem sets with worked solutions
- Notation reference sheet — a lasting resource
- Dedicated study channel during programme
- Completion record issued at close
Process Steps
Foundations: vectors, matrices and notation used in ML papers
Calculus for gradients: derivatives, chain rule, partial differentiation
Probability: distributions, expectation, variance — applied to model behaviour
Optimisation: gradient descent mechanics, loss landscapes, convergence intuition
Model Building Studio
A thirteen-week studio in which learners build four models of increasing ambition under weekly critique. The emphasis is on judgement rather than syntax: choosing an approach that fits the data, designing an evaluation that could actually fail, diagnosing training problems, documenting decisions and knowing when to stop. Each studio week ends with a public critique session where work is discussed openly and kindly. Aimed at learners with foundations already in place. Commitment is nine to eleven hours a week.
- Twenty-six sessions including all critiques
- GPU credits included — no separate sign-up
- Four model build-and-critique cycles
- Mentor pairing with structured check-in schedule
- Decision log template + portfolio review at close
Studio Cycle (repeated four times at increasing complexity)
Problem framing and dataset selection — defining what failure looks like
Model development with decision log maintained throughout
Evaluation design and results documentation
Public critique: presenting decisions and receiving structured feedback
Portfolio and Practice Programme
A ten-month part-time programme for learners preparing to work professionally in applied AI. It combines technical modules across data engineering, modelling and deployment with sustained work on three portfolio projects, each documented to a standard a reviewer can follow. The programme also runs practical sessions on technical writing, code review etiquette, whiteboard problem practice and how to talk through a project in an interview. Commitment is twelve hours a week.
- Weekly sessions across data engineering, modelling, deployment
- Three full project reviews with written assessor feedback
- Mentoring throughout the ten months
- Cloud credits included; interview practice workshops
- Final portfolio assessment + written reference summarising work done
Programme Arc
Months 1–3: Data engineering and pipeline fundamentals
Months 4–7: Modelling, evaluation and deployment — Project 1 and 2
Months 8–9: Technical communication, interview preparation, Project 3
Month 10: Final portfolio assessment and written reference
Which Programme Fits You?
The three programmes are designed to be taken in sequence, but that isn't required. Use this table to find where you fit now.
| Your situation | Maths Refresher | Model Building Studio | Portfolio Programme |
|---|---|---|---|
| You can code but maths notation slows you down | Best fit | — | — |
| You have ML foundations and want to build real models | — | Best fit | — |
| You want to build a portfolio for professional applied AI work | — | — | Best fit |
| Live sessions in Malaysian time zone | |||
| GPU / cloud credits included | — | ||
| Mentor pairing | — | ||
| Written reference at close | — | — |
Programme Standards
Data Privacy
Learner data is not shared with third parties for marketing. All information collected is used for programme delivery and communication only.
Pre-Cohort Curriculum Review
Syllabus and materials are reviewed before each new cohort. Learners in any one cohort follow the same reviewed version of the programme.
Session Recording Access
All live sessions are recorded and made available to enrolled learners within 24 hours of the session closing.
Cohort Size Limits
Intake sizes are capped so that critique and mentoring remain substantive. We run additional cohorts rather than increase sizes beyond what the format can support.
Honest Outcome Descriptions
All programme descriptions specify skills practised and artefacts produced. No income projections or employment guarantees are made or implied.
Defined Support Channels
Each programme has a dedicated study channel. Support is provided by the facilitation team during programme hours, not by bots or community volunteers.
Programme Fees
Maths Refresher
RM 470
Per cohort enrolment · 6 hrs/week
- 14 live sessions
- Weekly problem sets + solutions
- Notation reference sheet
- Study channel access
- Completion record
Model Building Studio
RM 1,620
Per cohort enrolment · 9–11 hrs/week
- 26 sessions including critiques
- GPU credits included
- 4 model build cycles
- Mentor pairing
- Decision log template
- Portfolio review at close
Portfolio Programme
RM 4,700
Per cohort enrolment · 12 hrs/week
- Weekly sessions — full arc
- Cloud credits included
- 3 project reviews
- Mentoring throughout
- Interview practice workshops
- Final assessment + written reference
Not Sure Which Programme Fits?
Tell us about your background and what you're hoping to be able to do afterwards, and we'll point you toward the right starting point.
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