Choose the Programme That Fits Where You Are
Three tracks covering different stages of AI development — from machine learning foundations to applied deep learning to working with language models. Each one is independent; you start where your background puts you.
Back to HomeOur Teaching Methodology
Each programme at Ilmu Labs is designed around a learning bench metaphor: a clear workspace where you take ideas apart, understand how they work, and put them back together in something functional.
Modules are sequenced so each one builds on the last. Exercises appear early in the programme — not as tests at the end — so you're working with concepts while they're fresh. Tutors are present throughout, available to answer questions and review submissions.
Recorded sessions mean you can revisit difficult material without waiting for the next session. Weekly milestones give structure without turning the programme into a sprint.
Sequenced Modules
Concepts introduced in the order they depend on each other.
Recorded Sessions
Revisit any session whenever you need a second pass.
Hands-On Exercises
Weekly exercises applied to real datasets and problems.
Tutor Feedback
Written comments on submitted work from active practitioners.
Foundations of Machine Learning
A calm introduction to the core ideas behind machine learning — from working with data to training simple models. Suited to those with some programming familiarity who want a solid grounding before moving to more advanced topics. Includes weekly guided exercises, recorded sessions, and small practice projects. Paced over eight weeks, with tutor support throughout.
What You'll Actually Build
- A data cleaning and exploration notebook using a real Malaysian dataset
- A trained classification model with evaluation metrics
- A short written explanation of your model's behaviour and limitations
Programme Steps
Data fundamentals
Loading, cleaning, and exploring structured data with Python
Core ML concepts
Supervised vs unsupervised learning, training and evaluation
Common algorithms
Decision trees, linear models, and simple ensemble methods
Practice project
Build, evaluate, and write up a complete ML model with tutor review
Who This Is For
- Professionals with basic Python skills looking to add ML to their toolkit
- Developers wanting to understand what ML engineers do
- Analysts looking to extend from statistical methods into predictive modelling
Who This Is For
- Learners who've completed a foundations course or equivalent
- Software engineers moving into ML engineering roles
- Data scientists adding deep learning to their existing skills
Applied Deep Learning
A hands-on programme covering neural networks and practical model building using common frameworks. Best suited to learners who have completed a foundations course or have comparable experience. Includes graded projects, code reviews, and regular feedback. Runs over twelve weeks at a manageable weekly commitment.
What You'll Actually Build
- An image classification model trained from scratch
- A graded project using transfer learning on a domain-specific dataset
- A code-reviewed submission with documented model decisions
Programme Steps
Neural network fundamentals
Architecture, forward pass, backpropagation, activation functions
CNNs and image data
Convolutional layers, pooling, and image classification pipelines
Transfer learning
Fine-tuning pre-trained models for new tasks
Graded project + code review
Build a full model pipeline and receive tutor code review
Generative AI and Language Models
A focused track on working with modern language models — covering prompting, fine-tuning basics, and building simple applications responsibly. Intended for those comfortable with Python and eager to apply their skills. Includes a capstone project reviewed by mentors, with honest, constructive feedback throughout.
What You'll Actually Build
- A prompt pipeline for a defined use case with documented reasoning
- A lightweight application using a language model API
- A capstone project reviewed by a mentor with written feedback
Programme Steps
How language models work
Transformers, tokenisation, and the mechanics of generation
Prompting techniques
Structured prompting, few-shot approaches, and chain-of-thought
Fine-tuning basics and responsible use
Adapting models and understanding bias, limitations, and risk
Capstone project
Build and present a language model application with mentor review
Who This Is For
- Python-comfortable developers curious about building with LLMs
- Product people who want to understand what's technically feasible
- ML practitioners adding generative AI to their existing background
Compare the Programmes
Each programme is independent — you don't have to start at Foundations. Use the table below to find the right fit.
| Feature | Foundations RM 150 · 8 weeks |
Applied Deep Learning RM 630 · 12 weeks |
Generative AI RM 400 · flexible |
|---|---|---|---|
| Prior Python needed | Basic | Intermediate | Comfortable |
| ML background needed | None | Foundations-level | Python + some ML |
| Graded projects | |||
| Code review by tutor | |||
| Capstone project | |||
| Tutor support |
Shared Standards Across All Tracks
Data Privacy Compliance
Learner data handled under Malaysia's PDPA 2010. We collect only what's needed to run your programme.
Annual Content Review
All programmes are reviewed annually and updated when tools or best practices change meaningfully.
Specific Written Feedback
Project reviews include comments that are specific and actionable — not generic pass/fail judgements.
One Business Day Response
Tutors aim to respond to learner questions within one business day during the programme period.
Responsible AI Coverage
Ethics, bias, and appropriate use are part of how we teach — not optional reading appended to the syllabus.
Open-Source Tool Stack
We use standard, widely-adopted libraries so skills transfer to real projects beyond the programme.
Programme Fees
All fees in Malaysian Ringgit. Listed upfront with no add-ons required for core content.
8-week programme
- Recorded sessions
- Weekly guided exercises
- Practice projects
- Tutor support
12-week programme
- All Foundations features
- Graded projects
- Tutor code reviews
- Written feedback on submissions
Flexible duration
- Recorded sessions
- Prompting + fine-tuning modules
- Mentor-reviewed capstone
- Tutor support
Not Sure Which Track to Choose?
Send us a message with a bit about your background. We'll suggest a starting point that makes sense for where you are right now.
Get in Touch