Structured courses from
Python to deployment.
Each track is self-contained, practical, and designed to end with something you built yourself. Choose the level that fits where you are now.
Back to HomeHow the tracks are built.
Each Tunas AI track follows the same structural pattern: introduce the concept, apply it in a small exercise, then close the week with a project-level task that ties the pieces together. By the end of each week, learners have something working — not just something read.
The weekly clinic session is built into every track. It is not an optional extra. The instructor runs through common sticking points, then opens for questions. Learners who cannot attend live can access the recording and submit questions asynchronously before the next session.
Course materials are designed around what we call the "side desk" approach: code examples and explanations sit beside each other throughout, so there is no need to switch between a lesson and a separate coding environment when following along.
All three tracks are delivered online. Students in Kuala Lumpur and across Malaysia join the same cohort. Office hours are Malaysian time, and the working examples are shaped around the regional technology context.
Starting Out with AI and Python
A relaxed entry point for newcomers. Over six weeks you learn Python basics, data handling, and how a model learns, with a weekly clinic for questions. The loft pace keeps each step calm and clear. You finish with a small project and a clear direction for what comes next.
What is covered:
- Python syntax, data types, functions, and file handling
- Pandas and NumPy for data preparation
- Introduction to how a model learns from data
- Weekly clinic and final small project
Week-by-week structure:
- 01Python setup and fundamentals
- 02Working with data structures and files
- 03Introduction to Pandas and NumPy
- 04Exploratory data analysis
- 05How a model learns — core concepts
- 06Final project and clinic review
Machine Learning Practice
A practical track for learners ready for real projects. Across ten weeks you build a steady workflow for data, training, and evaluation, completing two grounded projects with personal feedback. Small cohorts keep mentoring close. Recordings and a peer channel are included throughout.
What is covered:
- Scikit-learn for classification, regression, and clustering
- Feature engineering and data preprocessing pipelines
- Model evaluation: metrics, validation, overfitting
- Two projects with individual instructor feedback
- Peer channel and session recordings throughout
Track structure:
- 01–02Data pipeline and feature prep
- 03–04Supervised learning methods
- 05Project 1: Classification problem
- 06–07Evaluation and model selection
- 08–09Unsupervised learning and clustering
- 10Project 2: End-to-end ML workflow
Deep Learning Loft
An advanced track for developers ready to work with neural networks. Over fourteen weeks you study 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 is covered:
- PyTorch foundations — tensors, autograd, training loops
- CNN, RNN, and Transformer architectures
- Training strategy, regularisation, and debugging
- Model deployment — serving and inference basics
- Capstone project with instructor feedback and alumni access
Track structure:
- 01–03PyTorch core and neural network basics
- 04–06Convolutional and recurrent architectures
- 07–09Transformers and attention mechanisms
- 10–11Training strategy and debugging
- 12–13Deployment and inference
- 14Capstone presentation and review
Choosing the right track.
If you are unsure which track to start with, the table below outlines what each one includes. The tracks progress in difficulty; Track 01 is the right starting point if you have no Python background.
| Feature | Track 01 | Track 02 Popular | Track 03 |
|---|---|---|---|
| Duration | 6 weeks | 10 weeks | 14 weeks |
| Price (RM) | 960 | 1,410 | 1,870 |
| Weekly clinic | |||
| Session recordings | — | ||
| Peer channel | — | ||
| Projects | 1 | 2 | 1 capstone |
| Alumni access | — | — | |
| Best for | No coding background | Some Python, ready for ML | Comfortable with ML, want NN |
Shared protocols.
Data Privacy
Learner information is handled under Malaysia's Personal Data Protection Act 2010. No data is shared with third parties for marketing.
Curriculum Updates
Each track is reviewed before every cohort intake. Examples that have become outdated are replaced with current tools and patterns.
Recorded Content Security
Session recordings are stored on a private, access-controlled platform. Links are not publicly shareable and expire after the course period.
Clear Enrolment Terms
Payment, deferral, and refund conditions are provided in writing before any enrolment is confirmed. No post-payment changes to terms.
Responsive Support
Administrative enquiries are acknowledged within one working day (Mon–Fri, MYT). Technical questions are addressed before the next clinic.
Cohort Caps
When an intake reaches capacity, the next cohort is scheduled rather than expanding the group beyond a size that allows meaningful mentoring.
What each track costs.
Starting Out
- Python fundamentals
- Data handling with Pandas/NumPy
- Weekly clinic sessions
- 1 final project with feedback
ML Practice
- Scikit-learn workflows
- 2 projects with personal feedback
- Weekly clinic + peer channel
- Session recordings included
Deep Learning
- PyTorch + architecture studies
- Deployment and inference
- Capstone with full feedback
- Lasting alumni access
Not sure which track is right for you?
Send a message and we can help. Describe your background briefly and we will suggest the most appropriate starting point.
Get in Touch