Three Courses. Clear Scope.
Honest Descriptions.
Each Weightspace programme has a defined starting point, a set of learning objectives, and a realistic description of the time and effort involved.
Back to HomeHow We Structure Learning
Weightspace courses are built around a simple idea: you learn by working with code, not by watching someone else do it. Each lesson introduces a concept, provides a worked example, and then asks you to apply the idea in an exercise. That exercise is checked — either through an automated environment or, in the higher courses, through mentor code review.
We move through topics in a deliberate sequence. Foundations before application. Understanding before optimisation. Each course has a clear scope: it covers what it says it covers, and we don't pad the material to make it seem more than it is.
Concept introduction
Each topic is introduced clearly, with context for why it matters in AI development work.
Worked example with code
A complete, runnable example shows the concept in practice. You can read it, run it, and modify it.
Guided exercise
You apply the concept yourself, with a defined task. The exercise has a clear expected output.
Review and feedback
In the ML Projects and Mentorship courses, a mentor reviews your work and gives specific written feedback.
Coding Foundations for AI
A beginner course building Python and data-handling skills through short lessons and practice exercises. Designed for learners who are new to development and want a grounded, honest starting point. Steady practice is built into the structure — it's not a course you can skim.
- Python syntax, data types, and flow control
- Working with lists, dictionaries, and files
- Introduction to functions and modules
- Basic data handling with NumPy and Pandas
- Short exercises at the end of each lesson
Hands-On ML Projects
A project-driven intermediate course where learners build and evaluate machine learning models on real datasets. Includes code review from a mentor and a portfolio project you keep at the end. Suited to learners with basic Python experience who are ready to work with actual data.
- Exploratory data analysis on real datasets
- Model building with Scikit-learn
- Model evaluation: metrics, cross-validation
- Code review feedback from a mentor
- Final portfolio project to keep
Cohort Mentorship Track
An extended programme combining structured modules, regular one-on-one mentor sessions, and a small peer cohort. For dedicated learners who want ongoing guidance while building real AI development skills. We explain the workload and expectations clearly before anyone joins — and we hold a short conversation with each prospective learner first.
- Structured weekly learning modules
- Regular one-on-one mentor sessions
- Small cohort: meaningful peer interaction
- Detailed written feedback on your work
- Pre-enrolment conversation included
How the Three Courses Compare
| Feature | Coding Foundations | ML Projects | Mentorship Track |
|---|---|---|---|
| Prior coding needed | No | Basic Python | Some experience |
| Real dataset work | Intro level | ||
| Mentor code review | |||
| One-on-one mentor sessions | |||
| Portfolio project | |||
| Small cohort group | |||
| Price | ฿3,800 | ฿16,000 | ฿33,800 |
// best for
Coding Foundations
Learners with no prior programming experience who want a grounded, manageable start with Python.
// best for
ML Projects
Those who can write basic Python and are ready to work with real data, build models, and receive mentor feedback.
// best for
Mentorship Track
Dedicated learners who want extended, guided development and value regular mentor access alongside structured modules.
Our Teaching and Delivery Standards
Defined Learning Objectives
Every lesson states what it covers and what you should be able to do after completing it.
Learner Data Privacy
Enrolment and personal data is stored securely and not shared or sold to third parties.
Regular Content Updates
Course materials are reviewed and updated to reflect changes in tools, libraries, and practices.
One-Day Response Time
Queries from learners and prospective students receive a response within one business day.
Transparent Enrolment Terms
Full programme details and pricing confirmed before any payment or commitment is made.
Responsible AI Teaching
We include responsible development practices in our ML curriculum, not just model-building mechanics.
Course Pricing
All prices in Thai Baht. No hidden fees or add-ons after enrolment.
Coding Foundations for AI
฿3,800
- Full course access
- All exercises included
- Learner support channel
Hands-On ML Projects
฿16,000
- Full course access
- Mentor code review
- Portfolio project
- Learner support channel
Cohort Mentorship Track
฿33,800
- Structured weekly modules
- One-on-one mentor sessions
- Small peer cohort
- Full written feedback
- Pre-enrolment conversation
Not Sure Which Course to Start With?
We're happy to answer questions before you commit to anything. Send us a message and tell us a bit about your background.
Send a Message