Our Courses
Three tracks built for different starting points
From your first Python script to deploying a neural network — each programme is structured, time-bound, and supported by a small cohort.
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The commons approach to AI development learning
Every track follows the same rhythm: weekly material release, a live clinic session, cohort channel access, and graded projects with written feedback. The structure stays consistent across tracks — the depth changes.
Weekly release
New material arrives each week so the cohort moves together.
Live clinic
One session per week for open questions with the instructor.
Graded projects
Hands-on work assessed by the instructor, not automated.
Cohort channel
A focused peer space for questions and shared notes.
AI Starter Course
A welcoming start for newcomers. Over six weeks you learn Python basics, data handling, and core model ideas, with a weekly clinic for questions. The commons pace keeps things calm. You finish with a small project and a plan for next steps.
This track is designed for people who are curious about AI but have no programming background. The material assumes nothing and moves at a pace that allows questions to develop naturally.
- Python fundamentals — variables, functions, data structures
- Data handling with pandas and NumPy
- Introduction to model concepts — no maths degree required
- Weekly clinic for open questions
- Final project: a working notebook with your own dataset
How the six weeks are structured:
Best for:
- Professionals looking to understand AI tools used at work
- People considering a career change into data or AI roles
- Anyone who wants to learn Python in an applied context
Best for:
- Developers who know Python and want to move into ML
- Data analysts wanting to add modelling skills
- AI Starter graduates ready for the next step
Machine Learning Track
A practical programme for learners ready to build. Across eleven weeks you cover data work, training, and evaluation, completing two grounded projects with personal feedback. Small cohorts keep support close. Recordings and a peer channel are included.
The focus throughout is on understanding why models behave the way they do, not just running them. By the end, you should be able to approach a new dataset and make considered decisions about how to work with it.
- Feature engineering and data preparation pipelines
- Supervised learning — regression, classification, trees
- Model evaluation — cross-validation, metrics, overfitting
- Unsupervised methods — clustering and dimensionality
- Two graded projects with instructor feedback
- Clinic recordings available after each session
Deep Learning Commons
An advanced track for developers ready to study neural networks. Over thirteen weeks you cover architectures, training, and deployment, building a capstone with guidance. The close cohort keeps feedback thorough. Lasting access and a quiet alumni space support you afterwards.
This track works through the implementation details that matter in practice — gradient descent mechanics, regularisation choices, how to debug a model that's not converging. The capstone is a substantial project you design in collaboration with the instructor.
- Neural network fundamentals in PyTorch
- CNN architectures and training techniques
- Sequence models and attention mechanisms
- Model deployment — packaging, serving, monitoring
- Capstone project with guided scoping and review
- Lifetime access to materials and alumni channel
Best for:
- Developers with Python and ML basics ready for deep learning
- ML practitioners wanting to move into neural network work
- Machine Learning Track graduates ready to continue
Compare
Which track is right for you?
Use this table to help decide. If you're not sure, reach out — a short conversation usually makes it clear.
| Feature | AI Starter | ML Track | Deep Learning |
|---|---|---|---|
| Duration | 6 weeks | 11 weeks | 13 weeks |
| Price | RM 970 | RM 1,430 | RM 1,860 |
| Prior coding needed | None | Basic Python | Python + ML basics |
| Weekly clinic | |||
| Graded projects | 1 | 2 | 1 capstone |
| Clinic recordings | — | ||
| Alumni channel | — | — | |
| Lifetime material access | — | — |
Standards
Shared practices across all tracks
Privacy and data handling
Learner information is used only for administration. Malaysia's PDPA 2010 applies. No data shared for marketing.
Cohort-based curriculum review
Material is reviewed after each cohort. Sections where learners stall are rewritten rather than left as-is.
48-hour feedback window
Project submissions receive instructor feedback within 48 hours. No automated scoring for assessed work.
Open-source tooling only
All courses use Python and standard open-source libraries. No proprietary platforms or licensed tools required.
Cohort size limits
Each intake is capped to maintain response quality. When full, the next cohort opens rather than the intake expanding.
Transparent communication
Schedule, fees, and expectations are stated clearly before enrolment. No surprises after you join.
Fees
Clear pricing, no additions
Each fee covers the full course — clinic, peer channel, project feedback. No registration charges or platform subscriptions.
6 weeks
AI Starter Course
RM 970
- Python and data handling foundations
- Weekly clinic included
- One graded project
- Cohort peer channel
11 weeks
Machine Learning Track
RM 1,430
- Data work, training, evaluation
- Weekly clinic + recordings
- Two graded projects
- Cohort peer channel
13 weeks
Deep Learning Commons
RM 1,860
- Neural networks and deployment
- Weekly clinic + recordings
- Guided capstone project
- Lifetime access + alumni space
Not sure which track to start with?
Send a message and describe where you are now. We'll suggest something sensible without any pressure to commit.
Get in Touch