Three tracks. Different depth. Designed to fit where you are.
Detailed descriptions, pricing, and guidance to help you pick the right course for your background and what you want to build.
Back to HomeHow the courses are structured
Weekly content release
New material is released on a weekly basis — readings, exercises, and tasks. You work through them at your own pace within the week, with no required live attendance.
Assignment submission & review
Assignments are submitted through our platform and reviewed by instructors within five business days. Feedback is written and specific — not automated or generic.
Final project & completion
Each course concludes with a final project. In shorter courses, this is a focused deliverable. In the full-stack programme, it's a deployed application — something you've built and shipped.
AI Ethics and Responsible Practice
A focused course covering the ethical considerations that arise in AI development — fairness, accountability, transparency, and the practical questions of how these abstract concerns translate into design and engineering decisions.
The course is structured as a series of case studies paired with conceptual readings, with weekly written reflections and a small final project. Suited for both technical and non-technical learners willing to engage with the material thoughtfully.
- Frameworks for analysing fairness and bias in AI systems
- Vocabulary for engaging with accountability questions in design
- Practice in written ethical reasoning through weekly reflections
- A final project applying ethical analysis to a real-world AI case
- 01Weekly reading and case study review
- 02Written reflection submitted each week
- 03Instructor feedback on reflection within 5 days
- 04Final project: ethical analysis of an AI system
- 05Completion certificate issued
- 01Foundations — collaborative filtering and content-based models
- 02Hybrid approaches and evaluation methodology
- 03Hands-on assignments with public datasets
- 04Project scope defined and developed over final weeks
- 05Working recommender system submitted and reviewed
Recommender Systems and Personalization
A practical course covering the foundations of recommender systems — collaborative filtering, content-based approaches, hybrid models, and the evaluation considerations that distinguish recommender work from general supervised learning.
The course assumes prior ML coursework and includes hands-on assignments using public datasets. Includes a substantial final project where learners scope and build a working recommender for a domain of their choosing.
- Practical understanding of collaborative and content-based filtering
- Competence in evaluating recommender systems appropriately
- Experience with real datasets and their particularities
- A working recommender for a domain you choose
Full-Stack AI Application Programme
A long-form programme for learners building production-ready AI applications end to end — frontend, backend, model integration, and deployment. Expected commitment: twelve to fifteen hours per week over five months.
The programme is suited for learners with prior software engineering experience who want to add applied AI capability to their work. Includes structured weekly content, peer code review, mentor sessions, and a substantial portfolio project shipped at the close of the programme.
- End-to-end AI application — from model to deployed product
- Experience with peer code review across the cohort
- Four one-on-one mentor sessions
- Portfolio project that runs and is publicly accessible
- Completion certificate from Garuda Tech
- 01Foundations — AI integration patterns, API design, model serving
- 02Frontend integration — building interfaces that consume AI endpoints
- 03Production deployment — containerisation, monitoring, reliability
- 04Peer review sprint — structured code review with cohort
- 05Portfolio project shipped and reviewed by mentor
Which course is right for you?
Use this comparison to identify the track that fits your current background and what you'd like to develop.
| Feature | AI Ethics | Recommender Systems | Full-Stack AI |
|---|---|---|---|
| Best for | All backgrounds | ML practitioners | Software engineers |
| Duration | 6 weeks | 10 weeks | 5 months |
| Price | ฿4,200 | ฿16,800 | ฿35,000 |
| 1-to-1 mentor sessions | |||
| Peer code review | |||
| Final project shipped/deployed | |||
| Written instructor feedback | |||
| Completion certificate |
What every course has in common
PDPA-compliant data handling
All learner data is handled in accordance with Thailand's Personal Data Protection Act.
Curriculum revised each cohort
Content is reviewed before each new cohort and updated where the field has moved.
1-day email response
Enquiries and learner questions receive a response within one Bangkok business day.
Explicit prerequisites
Every course states prerequisites clearly and plainly before you commit to enrolment.
All-inclusive THB pricing
Fees are in Thai Baht with no add-ons, hidden costs, or modules gated behind additional payment.
Completion certificate
Digital completion certificates issued to learners who complete all assignments and the final project.
Course fees in Thai Baht
- 6 weeks of weekly content
- Written feedback each week
- Final project included
- Completion certificate
- 10 weeks of weekly content
- Hands-on dataset assignments
- Substantial final project
- Completion certificate
- 5 months structured content
- 4 one-on-one mentor sessions
- Structured peer code review
- Deployed portfolio project
- Completion certificate
Not sure which course is right?
Send us a note about your background and what you're hoping to develop. We'll help you identify which track makes sense.
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