Three Tracks. One Clear Methodology.
Each track is organised around a project that students build from scratch. The methodology is the same across all three: work on real data, document the decisions, evaluate the results.
Back to HomeHow the Coursework Is Structured
Orientation
Define the project scope, set up your environment and understand the dataset you'll be working with throughout the track.
Build
Work through the core technical modules — preprocessing, model design, pipeline construction — with instructor check-ins between stages.
Evaluate
Apply appropriate metrics, test against held-out data, interpret results and document what worked, what didn't and what you'd change.
Submit
Submit the complete project — code, documentation, evaluation — and receive written instructor feedback on the full submission.
Computer Vision Intensive
A project sprint covering image data, model training and evaluation for visual tasks. Practical and outcome-focused across a defined timeframe.
- Image dataset sourcing, annotation and preprocessing
- Model architecture selection and training cycles
- Evaluation using classification and detection metrics
- Documented deliverable with code and evaluation report
Applied Machine Learning Track
Project-led learning where students build, evaluate and document real models on practical datasets. Designed for learners ready to move from theory into hands-on work.
- Supervised and unsupervised learning approaches
- Feature engineering and dataset management
- Model evaluation, comparison and selection
- End-to-end project documentation from problem to results
Data Engineering Foundations
Coursework on pipelines, storage and preparing data so models can be trained reliably. A practical grounding in the work that supports applied AI.
- Pipeline design: ingestion, transformation, validation
- Storage formats and query patterns for ML workloads
- Data quality checks and schema management
- End-to-end pipeline project with documentation
Which Track Fits Your Situation?
| Feature | Computer Vision | ML Track | Data Engineering |
|---|---|---|---|
| Good starting point | Basic Python | ML familiarity | Basic Python |
| Core focus | Image models | Tabular models | Pipelines & storage |
| Project deliverable | |||
| Instructor feedback | |||
| Feeds into | — | Computer Vision | ML Track |
| Fee (฿) | ฿3,675 | ฿8,750 | ฿12,250 |
Shared Across All Tracks
Data Privacy
Student project data and personal information is handled responsibly and not shared with outside parties.
Consistent Rubric
All project submissions are reviewed against a defined set of criteria covering code, documentation and evaluation quality.
Current Materials
Course content is reviewed and updated to reflect changes in tools and methods that affect how students approach the project work.
Direct Communication
Questions are answered in scheduled sessions or via written channels. Students have access to the instructor, not a support queue.
Clear Terms
What each track covers, what it requires and what it costs is stated plainly before enrolment. There are no hidden conditions.
Iterative Improvement
Track structure is updated based on what students find confusing or where progress tends to stall. Changes follow from observation, not assumptions.
Not Sure Which Track to Start With?
Send a message explaining where you are with Python and what area you want to work in. We'll suggest the most practical starting point.
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