Three Tracks. One Connected Path Through AI Development.
From your first line of Python to deploying a neural network — each track builds on the last, and none of them rush.
← Back to HomeA Methodology Built Around Retention
Each Damai Labs track is designed with a specific learner stage in mind. The AI Starter Course assumes no prior background. The Machine Learning Track assumes basic Python and an appetite for building. The Deep Learning Sanctuary assumes comfort with ML concepts and a desire to go deeper.
Within each track, the session structure follows a consistent pattern: introduce a concept, demonstrate it, practise it in a guided exercise, then apply it in a project. The pace between these steps is deliberate — there is time to absorb before the next layer arrives.
All tracks include live sessions with recordings, a peer channel that stays active between sessions, and project work with written feedback. The AI Starter Course additionally includes a weekly clinic for accumulated questions.
Introduce
Concept presented clearly with context
Demonstrate
Live example showing the concept in use
Practise
Guided exercise with instructor present
Build
Apply in a real project with feedback
AI Starter Course
A calm introduction for newcomers. Over six weeks you learn Python basics, data handling, and core model ideas, with a weekly clinic for questions. The sanctuary pace keeps things peaceful. You finish with a small project and a plan for next steps.
What This Track Covers
- Python fundamentals: variables, functions, loops, and data structures
- Working with data using pandas and NumPy
- Introduction to what a machine learning model is and how it learns
- Running and evaluating a simple model using scikit-learn
- Weekly clinic to bring questions from the previous session
- Final project with written instructor feedback
Session Outline
Python foundations — writing and running your first programmes
Data handling — loading, cleaning, and exploring data
Model concepts — how a model learns from data
First model — build, train, and evaluate with scikit-learn
Model improvement — understanding what the results tell you
Project week — build and present your completed starter project
This track is right for you if
- You are new to programming or have only basic familiarity
- You want to understand how AI works before going deeper
- You prefer a supported setting with an instructor you can ask
- You want a concrete project to complete before moving to ML
This track is right for you if
- You know basic Python and want to start building ML systems
- You want to work through real datasets with a guide
- You value feedback on your work, not just a grade
- You are preparing for a data or AI role in the next 12 months
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.
What This Track Covers
- Data pipelines — cleaning, transforming, and preparing datasets
- Supervised and unsupervised learning methods
- Model training, validation, and evaluation with scikit-learn
- Feature engineering and selection strategies
- Two real-world projects with individual instructor feedback
- Session recordings and peer channel throughout the track
Track Structure
Data foundations — building robust data pipelines from scratch
Supervised learning — regression, classification, and evaluation
Project One — applied ML task with written feedback
Unsupervised learning, ensemble methods, and tuning
Project Two — end-to-end pipeline with presentation and feedback
Deep Learning Sanctuary
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.
What This Track Covers
- Neural network fundamentals using PyTorch
- Convolutional and recurrent architectures
- Training strategies, regularisation, and debugging
- Model deployment — packaging and serving a trained model
- Capstone project with close instructor guidance
- Lasting access to course materials and alumni channel
Track Structure
Neural network basics — layers, activations, and backpropagation
CNNs for vision tasks and RNNs for sequential data
Training at scale — tuning, regularisation, and diagnostics
Deployment — packaging and serving a model via an API
Capstone project — end-to-end deep learning application
This track is right for you if
- You are comfortable with ML concepts and want to go deeper
- You want to work with neural networks and PyTorch
- You are working toward a role that involves model development
- You want a capstone you can show as part of a portfolio
Side-by-Side Track Comparison
Use this to help decide where to begin — or write to us and we will help you figure it out.
| Feature | AI Starter RM 980 |
ML Track RM 1,400 |
Deep Learning RM 1,850 |
|---|---|---|---|
| Duration | 6 weeks | 11 weeks | 13 weeks |
| Prior knowledge needed | None | Basic Python | ML comfort |
| Projects included | 1 | 2 | 1 capstone |
| Weekly clinic | Peer channel | Peer channel | |
| Session recordings | |||
| Alumni access after | – | – | |
| Written project feedback |
What Applies Across All Three Tracks
Data Privacy
Learner data is held securely in line with Malaysian PDPA requirements. No data is shared for commercial purposes.
Reviewed Materials
Course content is reviewed before each new intake. Libraries and tools are checked against current professional standards.
Capped Cohorts
Each cohort is capped below 15 learners. When full, a waiting list opens for the following intake.
Written Feedback
All project submissions receive specific written comments. No rubric scores without explanation.
Not Sure Which Track to Start With?
Send a message and describe where you are — we will help you figure out which track makes sense. There is no pressure to commit.
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