Course Tracks
Three tracks. One clear path through AI development.
Each course is designed to be taken in sequence. You can start at any level that matches where you are — but the tracks build on each other so the full path is meaningful.
Back to HomeOur Method
How our courses are structured
We follow a consistent pattern across all three tracks: establish the foundation, apply it to real problems, receive feedback, revise, and build something finished.
Foundation lessons
Recorded lessons cover the concepts and tools you need — clearly, without skipping steps.
Write it yourself
You complete exercises and projects from scratch — no provided notebooks to fill in.
Mentor review
A mentor reviews your submissions and provides written feedback on what you sent.
Final project
Each track ends with a project you can show and explain to others.
Track 01 · Beginner · 8 Weeks
Intro to Python & AI
A beginner course covering Python programming and the basic ideas of artificial intelligence through small, practical projects. No previous programming knowledge required. The course starts from absolute zero and moves carefully, with written notes and recorded lessons to revisit at any time. Mentor feedback is included. A completion record is issued when you finish.
What you will cover
- Python syntax, data types and control flow
- Functions, file handling and basic data structures
- Introduction to NumPy and pandas for data work
- What AI and machine learning actually are at the code level
- Building and running a simple classifier
Process steps
- Weeks 1–3: Python foundations and data types
- Weeks 4–5: Working with data using pandas and NumPy
- Weeks 6–7: AI concepts and building a first model
- Week 8: Portfolio project with mentor feedback
Track 02 · Intermediate · 12 Weeks
Practical Machine Learning
An intermediate track focused on building real machine-learning projects using widely used open-source tools. Learners create models, evaluate them properly and document their findings. The track includes code reviews on your submissions, access to group discussion, and ends with a portfolio project. Suited to those already comfortable with basic Python.
What you will cover
- Data preparation and feature engineering
- Supervised learning with scikit-learn
- Model evaluation — metrics, cross-validation, baselines
- Handling real-world data problems (missing values, imbalanced sets)
- Final portfolio project: full ML pipeline from data to evaluated model
Process steps
- Weeks 1–3: Data preparation and exploratory analysis
- Weeks 4–6: Supervised learning models and tuning
- Weeks 7–9: Evaluation methods and error analysis
- Weeks 10–11: Real-world data challenges
- Week 12: Portfolio project with mentor code review
Track 03 · Advanced · 16 Weeks
AI Engineering & Deployment
An advanced programme on building and deploying AI applications. Covers the full path from data ingestion through model evaluation to serving a working application in a real environment. Includes peer support, mentor guidance on a capstone project and a completion record. Best suited to experienced programmers who already understand ML fundamentals and want to close the gap between "model on a laptop" and "something running in production".
What you will cover
- Data pipeline design and reliability
- Model packaging and versioning
- Serving models via REST APIs with FastAPI
- Container basics with Docker
- Monitoring deployed models and handling drift
- Capstone: a deployed AI application with an API endpoint
Process steps
- Weeks 1–4: Data pipelines and engineering patterns
- Weeks 5–8: Model packaging, APIs and FastAPI
- Weeks 9–11: Docker, containerisation and deployment basics
- Weeks 12–14: Monitoring and production concerns
- Weeks 15–16: Capstone project with mentor review
Choose Your Track
Which track is right for you?
Use this as a guide. If you are not sure, send us a message before enrolling.
| Intro to Python & AI | Practical ML Popular | AI Engineering | |
|---|---|---|---|
| Duration | 8 weeks | 12 weeks | 16 weeks |
| Price (฿) | 3,600 | 16,000 | 30,000 |
| Prior Python needed | No | Basic level | Intermediate+ |
| Mentor feedback | |||
| Code review | — | ||
| Deployment covered | — | — | |
| Best for | Complete beginners | Know Python, new to ML | Experienced coders |
How We Work
Standards across all tracks
Data privacy
We collect only what is needed to deliver the course and respond to enquiries. No learner data is shared or sold.
Twice-yearly updates
All course material is reviewed and refreshed every six months to keep tooling current and accurate.
Two-day response
Questions sent via the course forum or by email receive a response within two business days during office hours.
Open-source tools only
Every tool covered is free and open-source. No proprietary software or paid platforms are required.
Written course notes
Each lesson has accompanying written notes so you can read and reference the material without relying solely on video.
No inflated claims
Course descriptions state exactly what you build and how long it takes. We do not make claims about employment outcomes or income.
Pricing
Simple, one-time fees
Pay once for the track you choose. No subscription, no auto-renewal, no hidden charges.
Track 01
Intro to Python & AI
฿3,600
One-time · 8 weeks
- All lessons and written notes
- Mentor feedback
- Community forum access
- Completion record
Track 02 · Popular
Practical Machine Learning
฿16,000
One-time · 12 weeks
- All lessons and written notes
- Mentor feedback
- Code review on submissions
- Group discussion access
- Portfolio project + completion record
Track 03
AI Engineering & Deployment
฿30,000
One-time · 16 weeks
- All lessons and written notes
- Mentor guidance on capstone
- Peer support access
- Deployed capstone project
- Completion record
Ready?
Not sure where to start? Ask us.
Drop us a message and we'll point you to the right track based on where you are now.
Get in Touch