// Programme details
Three programmes. Clear scope. Stated prerequisites.
Each programme page below follows the same order: prerequisites, weekly structure, assessment, time commitment, tooling, what is not covered, price. This makes comparison straightforward.
← Back to HomeHow the programmes are delivered
All three programmes are delivered in person at the Menara Sentral premises. Live sessions are two per week for the ten-week and twelve-week programmes, with the short course running two evenings per week across three weeks. Sessions run from 7:30 to 9:00 PM on Monday and Wednesday evenings.
Each session is recorded. Recordings are accessible to enrolled participants for the duration of the programme via a private link. Access expires when the cohort ends.
Assessed work is reviewed by the instructor who taught the relevant content. Feedback is written and specific, returned within five working days of each submission deadline.
Foundations of Machine Learning
# Programme metadata
Duration: 10 weeks
Sessions: 2 × 90 min per week
Schedule: Mon & Wed, 7:30–9:00 PM
Cohort cap: 24 participants
Indep. work: ~6 hrs/week
Fee: RM 1,450
Extras: None
A ten-week evening programme covering linear algebra and probability refreshers, data handling with pandas, supervised learning from linear models through gradient boosting, model evaluation, cross-validation, and the common failure modes of leakage and drift. Does not cover deep learning or deployment.
Prerequisites
- Comfort writing Python functions, loops, and list comprehensions
- Ability to set up and activate a virtual environment
- Familiarity with reading tabular data (CSV, spreadsheets)
- Exposure to any introductory statistics material (secondary level adequate)
Assessment
- Four graded assignments, one every two to three weeks
- One final project reviewed line by line by the instructor
- No timed examinations
Tooling
Python 3.10+, pandas, scikit-learn, matplotlib, Jupyter. All free and open-source. No paid API accounts required.
What this programme does not cover
Deep learning, neural networks, model deployment, cloud infrastructure, time series analysis, natural language processing, computer vision, reinforcement learning.
Enquire about this programmeApplied Language Model Engineering
# Programme metadata
Duration: 12 weeks
Sessions: 2 × 90 min per week
Schedule: Mon & Wed, 7:30–9:00 PM
Cohort cap: Not specified
Indep. work: ~7 hrs/week
Fee: RM 2,900
Extras: ~RM 120 API costs (student paid)
A twelve-week programme for developers who are building on top of existing language models rather than training them. Covers prompt structure and evaluation, retrieval-augmented generation with vector stores, tool calling and structured output, context management, cost and latency budgeting, offline evaluation harnesses, and failure analysis with real logs. Includes a running project taken from prototype to a deployed internal service.
Prerequisites
- Working Python — able to write and debug scripts independently
- Basic familiarity with HTTP — understands request/response, headers, JSON
- Can use Git for version control at basic level (commit, branch, push)
Assessment
- Running project developed across the twelve weeks
- Milestones reviewed at weeks 4, 8, and 12
- Final deliverable: working deployed internal service
Tooling
Python 3.10+, LangChain or equivalent, at least one commercial LLM provider API (OpenAI, Anthropic, or Google — student chooses), a vector store (Chroma or Pinecone), and a deployment target. Third-party API costs approximately RM 120 over the term, paid by the student to the provider.
What this programme does not cover
Training or fine-tuning language models, model architecture, inference infrastructure at scale, multimodal inputs, mobile deployment, iOS/Android integration.
Enquire about this programmeShort Course: Data Preparation for Machine Learning
# Programme metadata
Duration: 3 weeks
Sessions: 2 evenings per week (4 workshops)
Schedule: Mon & Wed, 7:30–9:00 PM
Format: 4 live workshops + recorded refs
Assessment: 2 graded exercises
Fee: RM 620
Extras: None
A three-week short course, two evenings a week, focused on the part of ML work that consumes most of the time in practice: schema design, joining messy sources, handling missing values honestly, feature construction, leakage checks, dataset versioning and documentation of provenance. Delivered as four live workshops with recorded reference material and two graded exercises.
Prerequisites
- Basic Python — enough to load a CSV and iterate over rows
- Basic SQL — SELECT, JOIN, WHERE, GROUP BY
Who the short course suits
Analysts who work with data daily and want to formalise their approach to pipeline quality. Also used as preparation for the ten-week ML Foundations programme by participants who want more time with the data handling material before entering the full cohort.
Tooling
Python, pandas, DuckDB, Great Expectations (basic), DVC. All free and open-source. Sessions cover setup, so prior installation is not required.
What this course does not cover
Model training, evaluation, or deployment. Streaming data pipelines. Cloud data warehouses. Spark or distributed compute. Those topics belong in the ten-week programme or subsequent material.
Enquire about this courseChoosing between programmes
Each programme is self-contained. Use this table to find the right starting point.
| Criterion | Data Preparation (3 wk) | ML Foundations (10 wk) | LM Engineering (12 wk) |
|---|---|---|---|
| Python requirement | Basic (load CSV, iterate) | Solid (functions, envs) | Working (scripts, debug) |
| SQL requirement | Basic (SELECT, JOIN) | Not required | Not required |
| Prior ML knowledge needed | None | None | None (but useful) |
| Weekly time commitment | ~5 hrs (3 weeks) | ~9 hrs (10 weeks) | ~10 hrs (12 weeks) |
| Covers model training | No | Yes | No (uses existing models) |
| Covers LLM application building | No | No | Yes |
| Price | RM 620 | RM 1,450 | RM 2,900 + ~RM 120 API |
// Best for
Data Preparation
Analysts who handle data daily and want to formalise their pipeline approach, or anyone preparing to enter the ML Foundations cohort.
// Best for
ML Foundations
Developers and analysts who want to understand machine learning from the inside: how models work, how to evaluate them, and where they fail.
// Best for
LM Engineering
Developers who are already building applications and want to build reliable services on top of language models, with proper evaluation and cost management.
Fees
All fees in Malaysian Ringgit. No instalment plans currently available.
Short Course
Data Preparation for ML
RM 620
- 4 live workshops
- Recorded reference sessions
- 2 graded exercises with feedback
- No third-party costs
Ten-week programme
ML Foundations
RM 1,450
- 20 live sessions (90 min each)
- 4 graded assignments
- 1 final project with line-by-line review
- Cohort capped at 24
- No third-party costs
Twelve-week programme
LM Engineering
RM 2,900
+ ~RM 120 API costs (student paid)
- 24 live sessions (90 min each)
- Running project across full term
- 3 milestone reviews + final review
- Deployed service as final deliverable
Not sure which programme is right?
Send a brief note about your technical background and what you are trying to do. We will suggest the appropriate starting point or tell you if you need more Python practice first.
Get in Touch