In [1]:

Three courses. Full specifications below.

Duration, prerequisites, what is covered, how assignments work, and pricing — all written out so you can decide before contacting us.

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In [2]:

How the courses are run

Each course has a fixed syllabus, defined weekly sessions, and assignments that receive written code review. The three courses form a sequence — data engineering foundations, then transformer architecture, then systems engineering — but each can be taken on its own if the prerequisites are already met.

Assignments are not graded on a rubric. They receive written feedback covering whether the code is correct, readable, and defensible as an engineering choice. This takes longer than auto-grading and produces more useful information.

The Capstone Track adds a dedicated technical mentor, fortnightly architecture reviews with a second reviewer, and a closing technical presentation. It is structured as supervised independent work, not a sequence of lectures.

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Course 1 · 10 weeks · RM 550

Python and Data Engineering for AI Work

The parts of Python and data engineering that model work depends on: vectorised numerical code, memory profiling, columnar formats, reproducible environments, dataset versioning, and writing pipelines that fail loudly rather than quietly. Written for analysts and backend developers preparing to move into model work.

Includes:

Weekly live sessions
Nine assignments with written code review
Dataset audit exercise on real Malaysian open data
Environment reproducibility check on every submission
Written assessment record at close

Expect 6–8 hours per week. Prerequisites: comfortable with Python.

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Course structure (weeks):

1–2

Numerical computing

3–4

Memory & profiling

5–6

Columnar formats

7–8

Reproducible envs

9–10

Dataset versioning & audit

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Course 2 · 18 weeks · RM 2,120

Transformer Architectures and Language Model Training

Attention, tokenisation, positional schemes, pretraining objectives, supervised fine-tuning, and evaluation of language models — implemented from the paper rather than from a wrapper library. Suited to engineers comfortable with PyTorch and calculus notation.

Includes:

Shared cluster access throughout
Sixteen assignments with code review
From-scratch transformer trained on a public corpus
Evaluation harness the learner writes themselves
Weekly paper-reading sessions
Dataset licensing and consent session
Public write-up at close

Expect 12–15 hours per week. Prerequisites: PyTorch, calculus notation.

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Course structure (weeks):

1–3

Tokenisation & embeddings

4–6

Attention mechanisms

7–9

Full transformer impl

10–13

Pretraining run

14–16

Fine-tuning & SFT

17–18

Evaluation & write-up

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Course 3 · 26 weeks · RM 4,700

AI Systems Engineering Capstone Track

A supervised track for engineers building a substantial system: retrieval, serving, evaluation, monitoring, cost control, and an honest error analysis. Aimed at practitioners who already train models and now need the surrounding system to hold up under load.

Includes:

Dedicated technical mentor throughout
Fortnightly architecture reviews with a second reviewer
Cluster and inference budget throughout
Written evaluation methodology (external critique)
Defensive review of the learner's own system
Model risk, provenance, and responsible deployment module
Written incident post-mortem
Closing technical presentation to invited engineers

Expect 15–20 hours per week. Prerequisites: experience training models.

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Track structure (weeks):

1–5

Retrieval & indexing

6–10

Serving infrastructure

11–16

Evaluation & monitoring

17–22

Risk, cost & post-mortem

23–26

Defensive review & presentation

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Which course fits your situation

Use this to self-select. If you are unsure, the enquiry form is the right next step.

Feature	Python & Data Eng	Transformer Arch	AI Systems Capstone
Duration	10 weeks	18 weeks	26 weeks
Hours per week	6–8	12–15	15–20
Fee (RM)	550	2,120	4,700
Cluster access
Dedicated mentor
Architecture reviews
Best for	Analysts & backend devs moving toward model work	Engineers who want to implement from the paper	Practitioners who need the production system to hold up

In [7]:

Pricing

Course 1

Python & Data Engineering

RM 550

10 weeks
9 assignments + code review
Live weekly sessions
Written record at close

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Course 2

Transformer Architectures

RM 2,120

18 weeks
16 assignments + code review
Cluster access included
Paper-reading sessions
Public write-up at close

Enquire

Course 3

AI Systems Capstone

RM 4,700

26 weeks
Dedicated mentor
Architecture reviews (×13)
Cluster + inference budget
Closing technical presentation

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Standards across all courses

Data protection

Learner data is handled under Malaysia's Personal Data Protection Act 2010. Enrolment information is not shared with third parties for commercial use.

Code ownership

All code written during a course belongs to the learner. There are no platform licences or restrictions on what you do with work produced during the programme.

Honest scope statements

No claim is made about employment outcomes. Each course produces a defined set of work and a written record of what was completed. What that work leads to is the learner's to determine.

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Questions about prerequisites or timing?

Use the enquiry form and we will reply with a direct answer.

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