Every course
has a clear shape.
Three tracks, each with a defined duration, a stated weekly commitment and a published prerequisite list. No ambiguity about what you are signing up for.
How the curriculum is structured
Mathematical and coding foundations
The Foundations track covers the mathematics that practical ML work actually uses — linear algebra, probability and calculus — alongside Python for data work. Neither is treated as decoration; each concept is introduced where it is needed in the code.
Architecture and engineering knowledge
The Deep Learning track builds on layer one to cover network architectures, training at scale, experiment tracking and the engineering that surrounds a trained model. This is where theoretical knowledge becomes production fluency.
Applied project from start to deployment
The Capstone turns both layers into a single coherent project: framing a real problem, building a dataset, training and evaluating a model, deploying it and writing a technical report that explains every decision.
Foundations of Machine Learning
An eight-week evening course covering Python for data work, linear algebra as it is used in practice, gradient descent, regression, classification and honest model evaluation. Written for working adults with some programming background who want the mathematics to feel usable rather than decorative. Roughly eight hours a week, split between live sessions and coursework.
What is included
- Weekly graded exercises with written tutor feedback
- Code review from a tutor on each submission
- Private cohort forum — questions answered during the week
- Written record of course completion on finishing all work
Who this suits
Software developers, data analysts and operations professionals who have some programming background and want to build a solid base in machine learning before moving into more advanced material.
Applied Deep Learning Track
A sixteen-week track through neural network training, convolutional and sequence models, transformers, fine-tuning, and the engineering around them: data pipelines, experiment tracking, evaluation and cost. Suited to learners who finished a foundations course or work already as software engineers. Twelve hours a week including two live sessions.
What is included
- GPU credits for all coursework compute
- Four assessed projects with tutor code review on each
- Two live sessions per week with the instructor cohort
- Written record of course completion on finishing all work
Who this suits
Engineers who have completed a machine learning foundations course or have equivalent practical background and want to move into neural networks, transformers and the tooling around production deep learning systems.
Mentored Capstone & Portfolio Programme
A twenty-four-week programme in which each learner ships one substantial project end to end — from problem framing and data collection through training, evaluation, deployment and a written technical report. Intended for people who want a body of work to show rather than a list of topics studied.
What is included
- Weekly one-to-one mentoring with a practising engineer
- Fortnightly cohort reviews — present work, get feedback
- Portfolio preparation and interview practice workshops
- Cloud credits for training and deployment infrastructure
- Written record of course completion on finishing all work
Who this suits
Professionals with solid machine learning and engineering knowledge who want to build one substantial, deployed project that demonstrates what they can do in a real work context — rather than a list of completed modules.
Choosing the right track
The tracks are designed to follow one another but you can enter at the level that fits your current background.
| Feature | Foundations | Deep Learning | Capstone |
|---|---|---|---|
| Duration | 8 weeks | 16 weeks | 24 weeks |
| Weekly hours | ~8 hrs | ~12 hrs | ~12 hrs |
| Live sessions | |||
| Tutor code review | |||
| GPU credits | |||
| One-to-one mentoring | |||
| Portfolio project | |||
| Completion record | |||
| Price (RM) | 480 | 2,400 | 4,180 |
Best for →
Learning the foundations with some programming background
Most popular →
Moving into neural networks and production engineering
Best for →
Building a concrete project to demonstrate existing skills
Shared across all tracks
Data privacy
Learner data is used only for course administration. We comply with the Malaysian Personal Data Protection Act (PDPA) and do not share enrolment information with third parties.
Assessment standards
All assessed exercises are reviewed against a published marking rubric. Feedback is written and specific to the submitted work — not generated from a template.
Curriculum review cycle
Material is reviewed after each cohort and updated where learner questions indicate gaps. The curriculum is not static — it reflects what working AI practitioners actually encounter.
Not sure which track is right?
Send an enquiry and we will look at your background and let you know which track fits — and what to prepare if the next level is a step away.
Talk to the Team