Three Programmes.
One Direction.
From foundational machine learning through to applied production AI — each course is designed to take you somewhere specific, not just to cover ground.
How Our Courses Are Built
Each programme follows the same structural approach: written lessons introduce the material in a way that can be revisited, Python exercises apply the concepts directly, and project work puts the whole thing together in a context that approximates real use.
The three courses form a sequence, but they are also independently complete. You don't have to begin at Foundations if your background places you at a later stage. Prerequisites are stated clearly so you can make that assessment yourself.
Machine Learning Foundations
A structured introduction covering the core ideas of machine learning: supervised and unsupervised methods, model evaluation, overfitting and regularisation, and the mathematical background that underpins how these methods work. Exercises use Python throughout. Projects are small and applied, completed by the student at their own pace.
This course is suitable for learners who have some programming experience and want a careful introduction to the field — not a rapid overview, but a methodical one.
Deep Learning & Neural Networks Programme
An intermediate programme covering the architectures and training methods used in contemporary deep learning. Topics include feedforward networks, convolutional architectures for image processing, recurrent networks and attention mechanisms for sequential data. The programme combines theoretical material with substantial implementation work, including reproducing methods from foundational research papers.
Designed for learners who have completed the Foundations course or have equivalent background, and who want to develop genuine working knowledge of modern neural network techniques.
Applied AI Engineering Track
A practical track focused on the engineering work involved in taking AI models from development into production. Topics include data pipeline design, model serving infrastructure, monitoring and observability, and the operational considerations that separate a working prototype from a system that runs reliably in practice. The track is built around a series of real-world style projects developed over several months, with written feedback from the instructional team on each submission.
Suitable for learners with existing AI knowledge who want to develop the engineering side of the discipline — understanding not just how to train models, but how to deploy and maintain them.
Which course is right for you?
Compare the three programmes side by side.
| Feature | ML Foundations ฿2,500 |
Deep Learning ฿5,400 |
AI Engineering ฿7,900 |
|---|---|---|---|
| Level | Foundations | Intermediate | Advanced |
| Typical duration | 8–12 weeks | 16–20 weeks | 4–6 months |
| Python exercises | |||
| Instructor feedback on projects | |||
| Paper implementation work | |||
| Best for | Learners entering ML for the first time | Building deep learning expertise | Deploying and operating AI systems |
What applies across all three programmes
Data Privacy (PDPA)
Enrolment and learning data is handled in accordance with Thailand's Personal Data Protection Act. No data is passed to third parties for marketing.
Support Response Times
Technical queries: response within one business day. Substantive learning questions: within two business days. Support available in both English and Thai.
Content Updates
All enrolled students receive access to updated course content when significant revisions are made. The field moves fast; our material reflects that.
Written-First Format
Lessons are text-based, written to be read carefully rather than watched at speed. This applies to all three programmes without exception.
Secure Access
Course access is provided through a secure student account. Credentials are personal and non-transferable. Access is retained for the enrolled student.
Transparent Pricing
All prices are in Thai Baht. No subscription fees, no automatic renewals, no upsell push after purchase. What you pay for is what you get.
Course Fees
One-time payment per programme. No subscription required.
ML Foundations
- Full written course material
- Python exercises
- Applied mini-projects
- Support access
- Content updates included
Deep Learning
- Full written course material
- Python exercises and implementation
- Paper reproduction projects
- Support access
- Content updates included
AI Engineering
- Full written course material
- Multi-month project series
- Written instructor feedback
- Priority support access
- Content updates included
We'll help you find the right entry point
Send us a message describing your background and what you're looking to build — we can suggest which programme makes sense.
Book a Consultation