— Course Catalogue
Three Stages.
One Clear Direction.
Each Synaptiq course is a defined stage in a learning sequence. Whether you are starting from zero or looking to deepen existing knowledge, there is a course designed for where you are now.
Back to Home— Our Methodology
How the Signal Path Works
The Signal Path is Synaptiq's curriculum framework. It maps each concept to what precedes it and what depends on it — so nothing is introduced before the foundation is in place, and nothing is skipped because it seemed difficult.
Each of the three courses is a segment of this path. You move through them sequentially, confirming understanding at each stage before the complexity increases. You leave each course with something you have built — not just a test score.
Assessments are a mix of short written responses, coding exercises, and project submissions. There are no surprise examinations. Deadlines are structured but allow for reasonable flexibility when discussed with your mentor.
Stage 01
Programming for AI Basics
A welcoming introductory course covering Python essentials, data handling, and the core concepts behind common models. Designed for newcomers wanting a steady, structured start. Includes guided lessons, small practice tasks, mentor-supported feedback, and a certificate of completion.
What You Will Cover
- Python syntax, data types, and control flow
- Working with NumPy, Pandas, and basic datasets
- Introduction to supervised and unsupervised learning concepts
- Simple model training with scikit-learn
- Final data project with mentor review
How It Runs
Duration: Eight weeks
Format: Online or hybrid (Bangkok campus)
Weekly commitment: Approximately 6–8 hours
Includes: Lessons, practice tasks, mentor feedback, certificate
Stage 02
Hands-On Deep Learning
A practical, project-based course working with neural networks, modern frameworks, and real datasets. Suited to those with basic Python who want applied experience. Includes weekly build sessions, code reviews, a capstone project, and community access.
What You Will Cover
- Neural network architecture and training fundamentals
- Working with TensorFlow and PyTorch on real datasets
- Convolutional and recurrent network patterns
- Weekly build sessions with code review from instructors
- Capstone: a deployable deep learning model
How It Runs
Duration: Twelve weeks
Format: Online or hybrid (Bangkok campus)
Weekly commitment: Approximately 8–10 hours
Includes: Build sessions, code reviews, capstone, community access
Stage 03
Machine Learning Engineering Path
An extended programme covering model building, deployment basics, and collaborative workflows, with portfolio work throughout. Aimed at dedicated learners preparing for technical roles. Includes mentor guidance, applied projects, peer collaboration, and skills-focused career sessions.
What You Will Cover
- ML pipeline design and model lifecycle management
- Deployment approaches: APIs, containers, and cloud basics
- Collaborative Git workflows and code quality practices
- Portfolio-building across three applied projects
- Career-focused sessions on presenting technical work
How It Runs
Duration: Six months
Format: Online or hybrid (Bangkok campus)
Weekly commitment: Approximately 10–12 hours
Includes: Mentor sessions, three projects, career guidance, certificate
— Decision Guide
Which Stage Fits You
Use this table to find your starting point. If you are unsure, the introductory stage is always a sensible place to begin.
| Feature | Stage 01 ฿3,900 |
Stage 02 ฿16,800 |
Stage 03 ฿33,800 |
|---|---|---|---|
| Best for | Complete beginners | Basic Python users | Stage 02 graduates |
| Duration | 8 weeks | 12 weeks | 6 months |
| Mentor feedback | |||
| Certificate | |||
| Capstone project | |||
| Community access | |||
| Deployment training | |||
| Career sessions | |||
| Portfolio work |
Not sure where to begin? Send us a message — we will help you figure it out.
— Across All Courses
Standards Applied Consistently
Learner Data Privacy
All enrolment and progress data is stored securely. We follow Thai PDPA guidelines and do not share personal information for commercial purposes.
Responsive Support
Code review feedback is returned within 48 hours. Mentor queries receive a direct response — not an automated reply or a redirect to a knowledge base.
Curriculum Review Cycle
All course content is reviewed every six months. Tools, frameworks, and examples are updated to reflect what is current in AI development practice.
No Hidden Costs
The published fee covers everything: course access, materials, mentor sessions, and the certificate. There are no additional charges during the course.
Skills-Based Assessment
Completion is assessed on demonstrated work — submitted projects and exercises — rather than attendance records or passive participation alone.
Capped Cohort Sizes
We limit enrolment per intake deliberately. A manageable number of learners per mentor ensures the quality of feedback does not diminish as the cohort fills.
— Pricing
Clear, Flat Course Fees
Stage 01
Programming for AI Basics
฿3,900
- 8-week programme
- Guided lessons and practice tasks
- Mentor-supported feedback
- Certificate of completion
Stage 02
Hands-On Deep Learning
฿16,800
- 12-week programme
- Weekly build sessions and code reviews
- Capstone project
- Community access
- Certificate of completion
Stage 03
ML Engineering Path
฿33,800
- 6-month programme
- Mentor guidance throughout
- 3 applied portfolio projects
- Career-focused sessions
- Certificate of completion
— Ready to Begin
Not Sure Where to Start? Just Ask.
We are happy to talk through your background and recommend the right stage. There is no pressure — it is just a conversation.
Send an Enquiry