Three programmes, each designed for a specific stage of ML learning.
An introductory twelve-week programme, a focused two-day workshop on neural networks, and a monthly reading group for ongoing engagement with the literature. Full details and pricing for each are below.
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How each programme is structured
Written notebooks as the primary medium
Concepts are introduced in runnable Jupyter notebooks with explanatory prose between code cells. Exercises ask you to modify, extend, or debug the provided code — not copy and paste.
Recorded lectures for context and motivation
Each week's recorded lecture frames the notebook topic and explains the reasoning behind the design choices. Recordings are available after sessions for review.
Live sessions for questions and discussion
Office hours and workshop sessions run live online. These are not additional lectures — they are structured around questions from participants and discussion of what came up during notebook work.
Applied Machine Learning Foundations Programme
A structured twelve-week introductory programme for learners who already write Python comfortably and want a grounded introduction to applied machine learning. The programme covers data preparation, classical algorithms, model evaluation, basic feature engineering, and the working habits used in real practice.
Each week consists of a recorded lecture, a written notebook with exercises, and a small office-hour session for questions. Learners should expect to commit eight to ten hours per week. The programme is educational; it does not arrange employment and makes no claim about salary outcomes.
What the programme covers
- Data loading, inspection, and preparation
- Classical algorithms: linear models, trees, ensemble methods
- Model evaluation: cross-validation, metrics, pitfalls
- Feature engineering and selection
- Working practices: reproducibility, documentation, iteration
Twelve-week structure
Data fundamentals — loading, cleaning, visualising, and understanding datasets before modelling
Core algorithms — fitting and interpreting linear models, decision trees, and ensemble methods
Evaluation and iteration — honest performance assessment, avoiding evaluation mistakes
Applied project — bringing the full workflow to a new dataset with office-hour support
programme price
RM 4,580
Includes all notebooks, recorded lectures, and weekly office-hour access for twelve weeks.
prerequisite
Comfortable Python — functions, data structures, basic file I/O. NumPy or pandas experience helpful but not required.
commitment
8–10 hours per week for twelve weeks
Enquire About EnrolmentNeural Networks Weekend Workshop
A two-day weekend workshop for learners who have completed at least one course in applied machine learning and would like a careful, hands-on introduction to neural networks. The workshop covers the mathematics of a single neuron, forward and backward passes, common activation choices, and small training loops in PyTorch.
Participants work through a shared notebook in small groups, with breaks for discussion. The aim is a clear conceptual foundation, not a finished portfolio project. The workshop is delivered live online and recordings are available afterwards.
What the workshop covers
- The mathematics of a single neuron and simple networks
- Forward and backward passes in detail
- Activation functions and their practical implications
- Building and running small training loops in PyTorch
- Reading simple neural network code and understanding what it does
Two-day schedule
Day 1 — Neuron mathematics, forward pass, activation functions, and first PyTorch tensors
Day 2 — Backward pass, gradient computation, training loops, loss curves, and reading trained model outputs
workshop price
RM 680
Includes shared workshop notebook, both live sessions, and access to recordings afterwards.
prerequisite
At least one prior applied ML course. Familiarity with fitting and evaluating a model. Basic linear algebra helpful.
format
Two-day live online, weekend. Recordings provided after.
Enquire About EnrolmentNotebook Reading Group
A monthly evening reading group for self-taught learners who would like a calm, regular space to read a published paper or notebook together and discuss the ideas in plain language. Each session works through one short paper or one well-known notebook from the public literature, paced for steady understanding rather than coverage.
The group is conversational and educational; it does not offer career advice or assess individual skill levels. It is useful for learners who find they read more carefully when reading alongside others, and who want a regular occasion to engage with the primary literature.
What a typical session involves
- One paper or notebook, circulated in advance
- Facilitated reading with pauses for questions and discussion
- Plain-language explanations of notation and terminology
- Discussion of the ideas and their practical implications
- Approximately 90 minutes per session
Session process
Facilitator selects and circulates the paper or notebook at least five days before the session
Participants read individually and note questions or points of confusion
Group meets live (online, evening MYT) and reads through with discussion breaks
monthly subscription
RM 220 / month
One session per month. Cancel at any time before the next billing date.
prerequisite
Some ML background is helpful for engaging with the material, but there is no formal prerequisite check for the reading group.
format
Live online, one evening per month, approximately 90 minutes. Session materials circulated in advance.
Enquire About JoiningChoosing the right programme
A direct comparison to help you identify which programme fits where you are now and what you are trying to achieve.
| ML Foundations | NN Workshop | Reading Group | |
|---|---|---|---|
| Duration | 12 weeks | 2 days | Ongoing monthly |
| Price | RM 4,580 | RM 680 | RM 220/mo |
| Python prereq | Required | Required + ML background | Helpful, not required |
| Weekly hours | 8–10 hrs/wk | Full weekend | ~90 min/month + reading |
| Best for | Building ML foundations from scratch | Going deep on neural networks | Staying engaged with the literature |
Technical and educational standards
Commitments that apply across all three programmes.
Python 3, current scikit-learn and PyTorch
All notebooks are maintained against current stable versions of the key libraries. We update the material when APIs change rather than leaving outdated code in exercises.
Publicly licensed datasets only
Every exercise dataset is from a public source with a clear licence. We do not ask learners to use their employer's data for exercises, and we do not create exercises that require personal data submission.
Local execution, no proprietary platform
Exercises run in standard Jupyter on your own machine. There are no required accounts with cloud notebook providers, no browser-only execution environments, and no licence fees for the environment itself.
Pricing summary
All prices in Malaysian Ringgit. No hidden fees or add-ons.
ML Foundations
RM 4,580
12-week programme
- All weekly notebooks
- Recorded lectures
- Weekly office hours
- Notebook updates
NN Workshop
RM 680
2-day weekend
- Shared workshop notebook
- Both live sessions
- Recordings after
Reading Group
RM 220
per month
- Monthly 90-min session
- Materials in advance
- Cancel any time
Ready to discuss which programme fits?
Describe your Python background and what you are hoping to understand. We will outline the right programme and what to expect week by week.