Programmes
Three Programmes. Three Stated Scopes. Pricing Published.
A weekend workshop, a thirteen-week course in reinforcement learning, and a twelve-month enterprise retainer. Each one carries a reading strip with the numbers, and an honest scope paragraph with the limits.
Back to HomeInstructional Approach Across All Three Programmes
Every programme at Synthara Lab starts from the same design question: what does a person need to know before choosing this course? The answer — contact hours, self-study hours, prerequisite level, assessment format — appears on a reading strip before any descriptive text.
Within each programme, the same discipline applies to references. Where a benchmark or published finding is cited, the source is named. Where a topic is not covered, it is stated explicitly rather than implied by omission.
Assessment in the longer programmes uses implementation exercises and reproduction studies. The rationale is that reproducing a published result — or failing to reproduce it and documenting why — develops more practical understanding than answering questions about what should theoretically happen.
The enterprise retainer applies these same principles to organisational settings: a written, tailored curriculum, quarterly review, and a final capability report that the organisation can use internally.
Programme 01
Weekend Workshop: Building a First Model
A two-day workshop across one weekend, taking a small tabular dataset from raw file to a trained, evaluated, and documented model. The workshop covers data exploration, data leakage, baselines that matter, a first gradient-boosted model, honest evaluation metrics, and writing the model card. All work runs in a provided sandbox — participants need working Python and a laptop.
Total contact time is twelve hours across two days. There is no pre-reading, no homework, and no assessment. The course delivers one complete path end to end at an introductory pace.
What the two days cover
- 1 Data loading, initial exploration, and identifying data quality issues in the provided tabular dataset
- 2 Detecting and handling data leakage — the most common source of misleadingly good evaluation scores in applied ML
- 3 Building and evaluating baselines; understanding what a useful baseline actually measures
- 4 Training and evaluating a gradient-boosted model against the established baselines
- 5 Writing the model card: what the model does, what it does not do, and how it was evaluated
- Suitable for participants with Python experience but no ML background
- Sandbox environment provided — no setup required beyond Python and a laptop
- Covers a complete, real path — not a curated toy example
- Twelve contact hours across Saturday and Sunday
- Three implementation exercises — one per major topic cluster
- Final reproduction study with required written account of what did not replicate
- Benchmark limitation module with all references supplied
- Approximately 195 hours total (65 contact + 130 self-study)
Programme 02
Reinforcement Learning Fundamentals
A thirteen-week course covering the theoretical and applied foundations of reinforcement learning. Topics: Markov decision processes, dynamic programming, temporal-difference methods, policy gradients, actor-critic architectures, exploration strategies, and the practical difficulty of reproducing published RL results.
Weekly workload is five contact hours plus approximately ten hours of laboratory work. Assessment is by three implementation exercises distributed across the thirteen weeks and a final reproduction study, which requires a written account of what did not replicate and the reasons why.
Thirteen-week topic sequence
- 1 Markov decision processes and the formal problem structure
- 2 Dynamic programming — value iteration and policy iteration
- 3 Temporal-difference learning (TD, Q-learning, SARSA)
- 4 Policy gradients and the REINFORCE algorithm
- 5 Actor-critic architectures and advantage estimation
- 6 Exploration strategies and their trade-offs
- 7 Evaluation under stochasticity and benchmark limitations (with references)
Programme 03
Enterprise Training & Advisory Retainer
A twelve-month retainer for organisations building or expanding internal AI engineering capability. Designed for teams that need structured upskilling with a curriculum matched to their actual stack, not a generic course.
Included in the retainer: four quarterly training blocks (up to twenty staff each), monthly instructor office hours, a written curriculum tailored to the organisation's stack and refreshed each quarter, code review clinics against internal repositories, a documentation and evaluation template library, and a written annual capability report.
What is delivered across twelve months
- Q1 Curriculum scoping, initial training block (up to 20 staff), first code review clinic
- Q2 Curriculum refresh, second training block, office hours (monthly throughout), code review clinic
- Q3 Third training block, template library update, code review clinic
- Q4 Fourth training block, final code review clinic, written annual capability report delivered
- Written curriculum tailored to your organisation's stack — updated quarterly
- Code review clinics against your own internal repositories
- Monthly office hours with an instructor throughout the twelve months
- Annual capability report — a written document for internal use
- HRDC claimable for Malaysian organisations
Which Programme Fits?
A feature comparison to help you decide between the three programmes based on your current situation.
| Feature | Weekend Workshop |
RL Fundamentals |
Enterprise Retainer |
|---|---|---|---|
| Suitable for individuals | |||
| Suitable for organisations | |||
| No prior ML knowledge required | Varies | ||
| Formal assessment | |||
| Curriculum tailored to your stack | |||
| Duration | 2 days | 13 weeks | 12 months |
| Price | RM 450 | RM 3,050 | RM 4,700/mo |
Best for
Weekend Workshop
Someone with Python who has not yet built an ML model and wants to complete one full path in two days without committing to a longer programme.
Best for
RL Fundamentals
Someone with an ML foundations background who wants to work through RL systematically, including a formal assessment component and a reproduction study.
Best for
Enterprise Retainer
An organisation building internal AI engineering capability that wants a tailored curriculum, rotating cohorts, and code review against its own codebase across twelve months.
Standards Applied Across All Three Programmes
PDPA Data Handling
Participant data managed under Malaysia's Personal Data Protection Act 2010.
Named References
Where a benchmark or finding is cited in course material, the source is named. No unsourced summaries.
Published Syllabi
Full syllabus available before enrolment, including assessment criteria and topic week-by-week.
Quarterly Curriculum Review
Enterprise curriculum reviewed quarterly. RL course reviewed annually against current literature.
HRDC Registered
Enterprise training programmes claimable under HRDC for Malaysian-registered organisations.
Stated Scope Limits
Every programme carries an honest scope paragraph. Scope limits are reviewed whenever course content changes.
Pricing
All fees are in Malaysian Ringgit. No hidden components — what is listed is what you pay.
Individual · Beginner
Weekend Workshop
RM 450
per participant
- 12 contact hours
- Sandbox environment
- No assessment, no pre-reading
- One complete modelling path
Individual · Intermediate
RL Fundamentals
RM 3,050
per participant
- 65 contact hours over 13 weeks
- ~130 hours self-study
- 3 implementation exercises
- Final reproduction study
Organisational
Enterprise Retainer
RM 4,700
per month · 12-month contract
- 4 quarterly training blocks
- Up to 20 staff per block
- Tailored written curriculum
- Annual capability report
Not Sure Which Programme Applies?
Send a short message with your background and what you want to work on. We will give you a direct answer, including if none of the three programmes is the right fit.
Contact Synthara Lab