Three courses. Scope and pricing stated plainly.
A four-week short course, a two-day intensive, and a five-month part-time programme — each with its own syllabus, workload, prerequisites and price.
Back to HomeHow our courses are structured
Prerequisites layer
Every course starts with what you need to know already. This is the first layer. If the prerequisite doesn't match your situation, we'd rather you know before joining than after.
Workload layer
Session hours and homework hours are stated as integers alongside the course name. You can check them against your calendar before reading any description.
Scope layer
Each course includes a "not covered here" section. This is how you can tell whether a course fits what you're trying to learn, rather than discovering a gap halfway through.
Working with Language Model APIs
RM 890 · 4 weeks · 2 hrs/session + ~3 hrs homework/week
Prerequisite
You write JavaScript or Python professionally. No prior ML background needed.
A four-week short course for developers who need to build reliable features on top of hosted language models rather than train their own. The course covers prompt structure and why small wording changes move outputs, structured output and schema validation, tool calling, streaming, token accounting and cost control, caching, retries and graceful degradation when a provider is slow.
The section most tutorials skip: evaluation. How to tell whether a change actually improved anything rather than felt better. Every example is written in plain functions rather than class hierarchies, matching the codebase style most teams here work in.
What's covered
- Prompt structure and output determinism
- Structured output and schema validation
- Tool calling and streaming
- Token accounting, caching, retries
- Evaluation: measuring improvement objectively
Not covered here
Training or fine-tuning models. Deployment infrastructure. Reinforcement learning. Large-scale data pipelines.
Included
Live sessions, recordings, a reference repository, an evaluation harness you keep, and written feedback on two exercises.
Process steps
Weekend Intensive: Fine-Tuning Small Models
RM 1,340 · 2 days · 6 hrs/day · max 16 participants
Prerequisite
Comfortable with PyTorch basics. You should be able to run a training loop, not just read one.
A two-day intensive on adapting small open-weight models to a narrow task on modest hardware. Day one covers dataset construction — and why it dominates everything else — formatting, deduplication, splits, and honest baselines. Day two covers parameter-efficient adaptation, hyperparameter choices that matter versus those that do not, evaluation against the baseline, and knowing when the answer is that fine-tuning was the wrong tool.
You leave with a trained adapter, an evaluation report, and the notebooks. Narrow by design: this course does not cover large-scale training, reinforcement learning from human feedback, or deployment.
What's covered
- Dataset construction, formatting, deduplication
- Honest baselines before any adaptation
- Parameter-efficient adaptation (LoRA / QLoRA)
- Hyperparameter choices that matter
- Evaluation against baseline; go/no-go decision
Not covered here
Large-scale distributed training. RLHF. Deployment and serving. API wrappers around the adapted model.
Included
Sessions, recordings, notebooks, a compute credit allowance for the weekend, and a written review of your evaluation report.
Process steps
Machine Learning Engineering Programme
RM 4,600 · 20 weeks · ~12 hrs/week · part-time
Prerequisites
Professional software experience and familiarity with Python. A short technical conversation before enrolment makes sure the fit is right for both sides.
A five-month, part-time programme covering the engineering side of machine learning: data pipelines, versioning, feature stores, experiment tracking, reproducible training, model registries, packaging and serving, latency and cost profiling, canary releases, drift monitoring, and the operational habits that keep systems honest after launch.
Roughly 70 per cent of the time is project work. Each learner ships three reviewed projects and a capstone system that runs end to end. Reviews are done by practising engineers, line by line, in writing.
Syllabus areas
- Data pipelines, versioning, feature stores
- Experiment tracking, reproducible training
- Model registries, packaging, serving
- Latency / cost profiling, canary releases
- Drift monitoring and post-launch operations
Not covered here
Research-style modelling or training large foundation models. Prompt engineering for hosted APIs (that's the API course). Data science and analysis work that doesn't involve production systems.
Included
Twice-weekly live sessions, recordings, three reviewed projects, capstone with written review, mentor office hours, cohort channel, and a repository you own.
Process steps
Which course fits your situation
Three different starting points. Use this to decide which one to enquire about.
| Feature | LM API Course | Fine-Tuning Intensive | ML Engineering Prog. |
|---|---|---|---|
| Duration | 4 weeks | 2 days | 20 weeks |
| Price (MYR) | 890 | 1,340 | 4,600 |
| Live sessions | |||
| Written review | 2 exercises | eval report | 3 projects + capstone |
| Technical prereq | JS or Python dev | PyTorch basics | Software + Python |
| Compute included | weekend credits | ||
| Best for | Devs adding AI features | Devs adapting models | Devs owning ML systems |
Standards that apply to all three courses
Privacy
Learner data is used only to operate the course. We don't share personal details with third parties or add people to mailing lists without explicit consent.
Syllabus currency
Materials are reviewed before each cohort starts. If the field has changed enough to matter, the syllabus is updated.
Enquiry response
We aim to reply to all enquiries within one working day. If a course isn't the right fit, we'll say so clearly rather than proceed with enrolment.
Company invoicing
Invoices can be issued to companies. Enquire with the billing entity name and details and we'll sort the paperwork.
No outcome claims
We describe a syllabus, a workload and a review process. We make no claim about employment, earnings or career outcomes of any kind.
Recordings included
All live sessions are recorded and available to enrolled learners for the duration of the cohort. No separate charge for recording access.
Pricing
LM API Course
RM 890
per cohort / 4 weeks
- 4 live sessions + recordings
- Reference repository
- Evaluation harness (yours to keep)
- Written feedback on 2 exercises
Fine-Tuning Intensive
RM 1,340
per participant / 2 days
- 2-day live online intensive
- Recordings + notebooks
- Weekend compute credits
- Written review of eval report
ML Engineering Programme
RM 4,600
per cohort / 20 weeks
- Twice-weekly live sessions
- 3 reviewed projects + capstone
- Mentor office hours
- Cohort channel + repo ownership
Tell us what you're working with
Send a message with your technical background and what you're trying to do. We'll describe which course makes sense and why — or say honestly if none of them fit right now.
Send a Message