Three Courses, One Clear Direction
From your first lines of Python to a portfolio-ready AI system — the Lumengrid track is designed to take you there in steps you can actually manage, with support at each stage.
Back to HomeHow the Courses Are Structured
Written Notes
Each module starts with clear written explanation before any code is introduced.
Practical Notebooks
You work through exercises in Jupyter notebooks with real datasets.
Submit for Review
Assignments are submitted and returned with specific written feedback.
Weekly Q&A
Small group sessions where you ask about the material, your code, or decisions you're unsure about.
Getting Started with AI Development
A beginner course covering the programming and data fundamentals needed for machine learning. Taught through small practical tasks, with notes and practice notebooks included throughout. Runs self-paced over six weeks with weekly question sessions so you're never stuck for long.
What You'll Cover
- Python syntax and data structures for data work
- Working with tabular data using pandas
- Core ML concepts: training, evaluation, overfitting
- Building and evaluating a first classification model
- Reading and interpreting model output honestly
Course Steps
Set up your environment and write your first data-handling scripts.
Work through data exploration and cleaning with real tabular datasets.
Build a simple classification model and evaluate it carefully.
Submit your work, receive feedback, and review the next path forward.
฿3,900
Practical Machine Learning
An intermediate course on preparing data and building and evaluating models on realistic problems, with honest discussion of what works and what doesn't. Designed for learners with basic coding experience. Runs over ten weeks with reviewed assignments, provided datasets, and walkthroughs for tricky sections.
What You'll Cover
- Feature engineering and data preparation pipelines
- Selecting and comparing model types for different problems
- Cross-validation and performance metrics in depth
- Handling imbalanced data and common edge cases
- Writing a clear, honest evaluation of model results
Course Steps
Explore provided datasets and build preprocessing pipelines.
Train and compare multiple model types on the same problem.
Evaluate results thoroughly and discuss where your model falls short.
Complete a reviewed assignment synthesising the full workflow.
฿15,400
Reliable AI Systems Track
A thorough track on building and deploying dependable AI systems, structured around a portfolio project and sound engineering practice. For committed learners aiming at independent work. Runs over fourteen weeks with mentor sessions and code reviews. Includes a project framework and a progress record documenting your decisions.
What You'll Cover
- System design for AI pipelines: ingestion to inference
- Error handling, monitoring, and model drift detection
- Deployment to a simple production-like environment
- Version control and reproducibility practices
- Documenting decisions for a portfolio-ready project
Course Steps
Define your project scope with your mentor and set up your framework.
Build the core pipeline with code reviews at each checkpoint.
Deploy and test your system in a controlled environment.
Finalise your portfolio project and progress record for submission.
฿33,600
Which Course Is Right for You?
Use this table to see what each course includes. If you're unsure, contact us and we'll talk it through.
| Feature | Getting Started ฿3,900 |
Practical ML ฿15,400 |
Reliable AI Systems ฿33,600 |
|---|---|---|---|
| Duration | 6 weeks | 10 weeks | 14 weeks |
| Practice notebooks | |||
| Reviewed assignments | |||
| Weekly Q&A sessions | |||
| Provided datasets | |||
| Mentor sessions | |||
| Portfolio project | |||
| Progress record |
Best for:
Getting Started
No coding experience • Exploring the field • Building a foundation
Best for:
Practical ML
Some Python experience • Want to work with real data • Building ML skills
Best for:
Reliable AI Systems
Solid ML foundation • Aiming for independent project work • Wants a portfolio
Shared Across All Courses
Data Privacy
Learner data is stored securely and not shared with third parties. Your assignment submissions are read only by the instructor reviewing your work.
Updated Every Six Months
Notebooks, code examples, and walkthroughs are reviewed and updated twice a year. Enrolees in active courses are notified when content changes.
Human Feedback
All assignment feedback is written by a person, not generated automatically. The goal is comments that are actually useful for your specific work.
12-Month Access
Course materials are accessible for twelve months from the date of enrolment. No pressure to finish by an arbitrary deadline.
Open-Source Toolchain
All courses use freely available tools. No proprietary software or paid subscriptions are required to complete the coursework.
Support in English and Thai
Written materials and course sessions are in English. For direct questions, you can write to us in Thai and receive a response in the same language.
Course Fees
All prices in Thai Baht. Each fee covers the full course: materials, notebooks, datasets, reviewed assignments, Q&A access, and twelve months of access to recorded sessions.
Beginner
Getting Started
6 weeks · Self-paced
- All written modules and notes
- Practice notebooks
- Assignment review
- Weekly Q&A sessions
- 12-month access
Intermediate
Most detailedPractical ML
10 weeks · Self-paced
- Everything in Getting Started
- Provided real-world datasets
- Section walkthroughs for complex topics
- Multiple reviewed assignments
- 12-month access
Advanced
Reliable AI Systems
14 weeks · Portfolio track
- Everything in Practical ML
- Mentor sessions included
- Code reviews at each checkpoint
- Portfolio project framework
- Progress record document
Not Sure Where to Start? Let's Figure It Out Together.
Write to us briefly about where you are now — coding experience, what you want to be able to do, and any constraints on your time. We'll suggest the right course and answer any questions you have before you commit.
Send a Message