Three Programmes · Kuala Lumpur
The complete course specifications — module, workload, assessment, price.
Everything you need to decide before enrolling. Read the scope first; we will take questions once you have.
Back to HomeOur Methodology
How these programmes are structured
Each programme starts from the question of what a developer needs to understand before the next concept makes sense. This produces a dependency order: topics are sequenced so that each one builds on what came before it. The order is documented and explained to participants at the start of the programme, not assumed.
Assessment is by work product, not progress completion. The Foundations course uses three graded assignments and a written reflection; the LLM Engineering course uses a deployed project and a fifteen-minute defence session. Both are reviewed by a tutor who reads every submission. The Team programme uses supervised internal project work reviewed in code clinics.
01 — Part-time · 12 weeks
Foundations of Machine Learning
A twelve-week part-time course covering linear algebra and probability as they are actually used, gradient-based optimisation, regression and classification, regularisation, evaluation methodology, and the common failure modes of a first model. Assumes comfort with Python and basic scripting; a short self-assessment is published so applicants can judge readiness themselves.
Workload
- 4 contact hours per week (live cohort sessions)
- 6–8 hours self-study per week
- Assessment: 3 graded assignments + written reflection
- Cohort cap: 24 seats
What the course covers
- Linear algebra and probability in the context of model training
- Gradient-based optimisation — what it does and where it fails
- Regression, classification, and regularisation methods
- Evaluation methodology — how to measure whether a model is behaving correctly
- Common failure modes of a first model
Process
- 01. Self-assessment checklist — confirm you meet the Python prerequisite
- 02. Enquiry and cohort date confirmation
- 03. 12 weeks of weekly live sessions plus self-study exercises
- 04. Three graded assignments reviewed by your tutor
- 05. Written reflection, then written record of completion issued
Course fee
RM 1,900
Prerequisite
Comfort with Python and basic scripting. A self-assessment checklist is published on this page — complete it yourself before getting in touch.
Completion record
A written record of completion is issued to participants who finish the course and meet the assessment criteria. This is not an academic qualification and does not carry accreditation.
Prerequisite
Prior completion of a foundations course in machine learning, or demonstrable equivalent experience. A self-assessment checklist is available to help you evaluate your readiness.
Assessment format
A working project deployed to a sandbox environment, followed by a fifteen-minute review session with your tutor. No slides — the project runs during the review.
02 — For developers · 10 weeks
Applied Language Model Engineering
A ten-week course for developers already comfortable with machine learning fundamentals. Covers tokenisation, transformer architecture, fine-tuning and parameter-efficient methods, retrieval-augmented generation, evaluation and red-teaming for reliability, prompt and context design, cost and latency budgeting, and deployment behind an API. Includes a module on published safety and governance practice, presented with sources named.
Workload
- 6 contact hours per week
- ~10 hours project work per week
- Assessment: deployed project + 15-min tutor review
- Prerequisite: ML foundations required
Topics covered
- Tokenisation and transformer architecture in depth
- Fine-tuning and parameter-efficient methods (LoRA, adapters)
- Retrieval-augmented generation — design and evaluation
- Red-teaming for reliability and prompt design
- Cost and latency budgeting for production API deployment
- Safety and governance practice, with sources named
Course fee
RM 2,850
03 — Business · 16 weeks
Team Capability Programme
A sixteen-week programme delivered to a single employer's engineering team of up to fifteen people. Begins with a capability review and a syllabus tailored to the team's existing stack, then runs weekly live sessions, code review clinics against the team's own repositories, and a supervised internal project taken from concept to a deployed service. Includes a written curriculum handover, recorded sessions retained by the employer, a module on evaluation and monitoring practice, and two follow-up clinics at three and six months.
Scope
- Up to 15 seats per team cohort
- Code review clinics against your own repositories
- Follow-up clinics at 3 and 6 months after completion
- Written curriculum handover included
- Recorded sessions retained by employer
Programme sequence
- 01. Capability review — written assessment of the team's current stack and skills
- 02. Syllabus design — tailored to the team's existing tools and internal project
- 03. 16 weeks of weekly live sessions and code review clinics
- 04. Supervised internal project from concept to deployed service
- 05. Written curriculum handover and session recordings delivered
- 06. Follow-up clinics at 3 and 6 months
Standard 15-seat engagement
RM 4,600
Scoped per cohort following capability review
Who this is for
An engineering team at a single employer that wants to build AI capability in-house, use its own codebase as the learning context, and have documented evidence of what was taught. The programme is not run as a multi-company cohort — it is delivered exclusively to your team.
Pricing note
The listed figure reflects a standard fifteen-seat engagement. Different team sizes or requirements are discussed before a figure is agreed. Contact us to start that conversation.
Decision Guide
Which programme fits your situation
| Feature | ML Foundations | LLM Engineering | Team Programme |
|---|---|---|---|
| Duration | 12 weeks | 10 weeks | 16 weeks |
| Contact hours/week | 4 hrs | 6 hrs | Weekly sessions + clinics |
| ML prerequisite required | Python comfort only | Yes — ML foundations | Assessed via capability review |
| Assessment method | 3 assignments + reflection | Deployed project + defence | Supervised internal project |
| Cohort cap | 24 seats | 24 seats | Up to 15 (your team only) |
| Customised to your stack | — | — | Yes, after capability review |
| Code review against your repos | — | — | Yes |
| Follow-up clinics after completion | — | — | At 3 and 6 months |
| Price (RM) | RM 1,900 | RM 2,850 | RM 4,600 |
Best for
ML Foundations
Developers with Python experience who want a solid grounding in how machine learning models are trained and evaluated.
Best for
LLM Engineering
Developers who already understand ML fundamentals and want to work specifically with large language models in a production deployment context.
Best for
Team Programme
An employer whose engineering team wants to build AI capability together, using their own codebase and internal project work as the learning context.
Shared Standards
What applies across all three programmes
Data handling
Enquiry and enrolment data is used only for course administration. Not shared with third parties for marketing. Full details in our Privacy Policy.
Full syllabus published
Every module and its position in the dependency order is published before any enrolment step. Nothing is held back for the course itself to reveal.
Individual tutor feedback
Cohort caps and team limits exist to make individual feedback possible. Every assessment component receives a written response from a tutor.
No accreditation claims
Completion records are stated clearly as records of attendance and assessed work. We do not claim they are academic qualifications or carry professional accreditation.
MYT-compatible scheduling
Live sessions are timed for the Malaysia time zone. Kuala Lumpur participants can request to attend certain sessions at the Jalan Pinang office with prior arrangement.
Curriculum reviewed between cohorts
Course content is reviewed before each new intake, particularly for the LLM Engineering programme where tooling and practice evolve between cohorts.
Pricing
Fixed fees for defined scopes
All prices in Malaysian Ringgit. No additional charges after enrolment. Team programme pricing discussed before agreement.
01
ML Foundations
12-week part-time · up to 24 seats
RM 1,900
- 4 contact hours/week (live)
- 3 graded assignments + reflection
- Individual tutor feedback
- Written completion record
02
LLM Engineering
10-week · prereq: ML foundations
RM 2,850
- 6 contact hours/week (live)
- Deployed project + 15-min defence
- Safety and governance module
- Individual tutor feedback
- Written completion record
03
Team Programme
16-week · up to 15 seats · business
RM 4,600
Standard 15-seat engagement
- Capability review + tailored syllabus
- Code review clinics (your repos)
- Supervised internal project
- Written curriculum handover
- Follow-up clinics at 3 and 6 months
Ready to ask the next question?
Prerequisites, cohort dates, team programme scope — send a message and we will answer directly.
Send a Message