Three tracks for three stages of AI development
Whether you are new to Python or ready to work on neural network deployment, there is a Kasturi Tech programme designed for where you are.
← Back to HomeA common methodology across all three tracks
Lay out the logic
Each topic begins with the reasoning behind it — why this method, what problem it addresses, what its limits are.
Work through examples
Worked examples are completed together in session, with space for questions at each decision point.
Build and submit
Learners build their own project, applying the methods from the track to a problem of their choosing within a given scope.
Receive and act on feedback
Project feedback is specific. Each response addresses the learner's actual choices and suggests one clear direction forward.
AI Starter Course
A welcoming start for newcomers to both Python and AI. Over six weeks you learn the essentials of Python, how to work with data, and how core model ideas are structured. Each session is built around questions that commonly arise, and a weekly clinic gives space for the ones that come up between sessions. The track ends with a small personal project and a written plan for what to do next.
What you cover
- Python basics — types, loops, functions, and file handling
- Working with tabular data using pandas and NumPy
- Core model concepts — what a model is, what training means, how accuracy is measured
- Building and submitting a small end-of-course project
- Weekly Q&A clinic throughout
How the sessions run
Machine Learning Track
A practical programme for learners who have Python experience and want to start building models properly. Across eleven weeks you cover the full cycle of a machine learning project — data preparation, training, evaluation, and iteration. You complete two grounded projects with personal feedback on each. Small cohorts keep the support close. Recordings and a peer channel are included so that learning continues between live sessions.
What you cover
- Data cleaning, feature engineering, and preparation pipelines
- Supervised and unsupervised learning methods
- Training loops, validation strategy, and evaluation metrics
- Two full projects with individual written feedback
- Peer channel and session recordings throughout
Suitable if you
- Have completed the AI Starter Course or have equivalent Python experience
- Want to build end-to-end ML projects, not just follow notebooks
- Can commit to two to three hours per week outside of sessions
Deep Learning Reasoning Room
An advanced track for developers ready to study neural networks in depth. Over thirteen weeks you cover architectures, the reasoning behind training decisions, and the realities of deployment. You build a capstone project with close guidance from instructors. The small cohort means feedback is thorough — not a pass or fail, but a conversation about your specific choices. Learners who complete this track retain lasting access to materials and a quiet alumni space.
What you cover
- Neural network architectures — feedforward, convolutional, recurrent
- Training dynamics, regularisation, and gradient flow
- Deployment: serving models and managing inference in production
- Capstone project with guided review at each stage
- Lasting access to materials and alumni space on completion
Suitable if you
- Have completed the Machine Learning Track or have equivalent hands-on ML experience
- Want to build models that go into real systems, not just study environments
- Are comfortable working independently between sessions
Feature comparison across the three courses
| Feature | AI Starter | ML Track | DL Reasoning Room |
|---|---|---|---|
| Duration | 6 weeks | 11 weeks | 13 weeks |
| Price (RM) | 960 | 1,430 | 1,860 |
| Python required | None | ||
| Weekly clinic | |||
| Session recordings | — | ||
| Peer channel | — | ||
| Projects with feedback | 1 | 2 | 1 capstone (staged) |
| Alumni space access | — | — | |
| Best starting point if | New to Python & AI | Python comfortable, new to ML | ML experience, ready for deep learning |
Not sure which fits? Send us a message and we will help you decide.
What every course holds to
Curriculum updated every cohort
Materials are reviewed after each group completes the course. Anything that generated repeated confusion is rewritten before the next intake.
Learner data privacy
Project submissions, questions, and personal information are kept within the course and not used for any other purpose. No marketing use of learner data.
Feedback within one working day
Questions submitted to clinic or by message receive a response within one working day. Project feedback is returned within three working days of submission.
Cohort size enforced at 12
When a cohort reaches twelve learners, enrolment closes and the next intake is opened. This is the number that keeps feedback quality where it needs to be.
Current tooling only
The libraries and frameworks used in each course are reviewed before each cohort to ensure they reflect what the AI development field is actually using today.
Completion records issued
Kasturi Tech issues a completion record for each course, aligned to the MQA professional development framework. Records can be shared with employers on request.
Clear fees, complete packages
All materials, recordings, feedback, and clinic access included. No separate charges.
AI Starter Course
- 6 live sessions
- Weekly clinic
- Written materials
- 1 project with feedback
- Next-steps plan
Machine Learning Track
- 11 live sessions
- Weekly clinic
- Session recordings
- 2 projects with feedback
- Peer channel access
Deep Learning Reasoning Room
- 13 live sessions
- Weekly clinic
- Session recordings + peer channel
- Capstone with staged reviews
- Lasting alumni space access
Not sure which track fits?
Send a message and we will ask a few questions about where you are and what you are hoping to build. Then we will suggest the right starting point.
Send an Enquiry