Three courses. One coherent path into AI.
From statistical foundations to applied project work — each course is designed to build on the last, and to be studied at a pace that suits working professionals.
Back to HomeHow courses at Khwamru Lab are structured
Each course begins with the conceptual foundation — why the method works, and in what conditions it becomes unreliable. Only once that grounding is established does the course introduce code, syntax, and tools. This order is deliberate: learners who understand the reasoning are better placed to debug, adapt, and extend what they build.
Every week includes guided exercises drawn from realistic scenarios. These are reviewed by a mentor, who provides written feedback explaining not just what to correct but why the correction matters. The process is slow by design.
Reasoning before syntax. Concepts before code.
Weekly tasks tied to real-world scenarios.
Written review on every submission.
Each course builds naturally on the previous.
Statistics for Machine Learning
A clear, patient course on the statistical thinking that underpins sound AI work. Covers probability distributions, hypothesis testing, regression, and model evaluation — explained step by step, with genuine attention to where things go wrong. Suited to learners with basic mathematics and some programming familiarity.
What you work through:
- Probability distributions and how they arise in data
- Estimation, confidence, and inference
- Regression and its assumptions
- Model evaluation metrics and their limitations
- A small end-of-course project with mentor feedback
How the course runs:
Modern Language Models
A practical course on how modern language models work and how to use them responsibly — explained carefully throughout. Covers transformer architecture, tokenisation, prompting, fine-tuning concepts, and the practical and ethical considerations of deployment. Suited to those comfortable with Python and core machine learning ideas.
What you work through:
- How transformer models represent and process language
- Prompting strategies and their tradeoffs
- Fine-tuning and retrieval-augmented generation
- Evaluation of model outputs and failure modes
- Responsible deployment considerations
How the course runs:
Applied AI Mentorship Track
An extended, mentor-led track where learners build and refine a real AI project at a pace that fits their lives. Suited to those who have established foundations in machine learning and want to develop something of genuine substance. The output is a portfolio piece — chosen by the learner, developed collaboratively, and grounded in their own context.
What the track includes:
- One-to-one mentoring sessions (scheduled flexibly)
- Project scoping and direction support at the outset
- Regular reviews of project progress and code
- Feedback on methodology, writing, and presentation
- A completed portfolio piece at the end
How the track runs:
Which course fits your current stage?
| Feature | Statistics for ML | Language Models | Mentorship Track |
|---|---|---|---|
| Duration | ~7 weeks | ~9 weeks | ~4 months |
| Price (฿) | 4,300 | 10,700 | 17,100 |
| Mentor feedback on exercises | |||
| One-to-one sessions | |||
| Portfolio project | |||
| Project scope chosen by you | |||
| Best starting point if new to AI | |||
| Good starting point | Next step | Most comprehensive |
Not sure which to begin with? Send us a message — we'll suggest a starting point based on your background.
What holds across all three courses
Biannual content review
All course materials are reviewed against current practice every six months. Outdated methods are updated; new developments are introduced when appropriate.
PDPA-compliant data handling
Learner data is collected only for course delivery purposes, handled securely, and managed in line with Thailand's Personal Data Protection Act.
Direct mentor access
Every learner has a named mentor reachable by message during the course. No forum queue. No automated replies.
Ethical framing in every topic
AI limitations, bias risks, and responsible deployment are discussed within each lesson — not siloed into a separate ethics module.
Explained feedback on exercises
Submissions receive written responses that explain the reasoning behind corrections, not just what to change. Feedback arrives within 72 hours.
Flexible scheduling
No fixed daily timetable. Courses are structured for part-time study alongside professional and personal commitments.
One price. No extras.
Statistics for ML
- All course materials
- Guided exercises
- Mentor feedback on submissions
- Small final project
Language Models
- All course materials
- Hands-on API projects
- Mentor feedback on submissions
- Applied end-of-course project
Mentorship Track
- All course materials
- One-to-one mentor sessions
- Async feedback throughout
- Portfolio project with review
All prices in Thai Baht (฿). No hidden fees. Payment details discussed on enquiry.
Have a question before you decide?
Write to us with your background and what you are hoping to work towards. We will suggest the most sensible starting point honestly — and without any pressure.
Get in Touch