Three courses, three different starting points
From first contact with Python to a deployable AI portfolio — the right course depends on where you are now, not where you want to be in the abstract.
How the courses are structured
Each Witcha Labs course is built around weekly study blocks and practical tasks. Concepts are introduced in the context of a problem to solve, not as standalone lectures. You work through increasingly complex exercises, receive mentor feedback on each stage, and build toward a final project.
The three courses form a natural sequence: each one builds on the previous. It is not required to take them in order, but the Getting Started course genuinely prepares you for the material in Practical ML Projects, and that course for the Full-Stack Track.
Getting Started with AI Programming
A gentle entry into coding for AI, covering Python basics, working with data, and the core ideas of machine learning — taught in small, clear steps. Short lessons and friendly exercises let you build confidence gradually, with mentor support along the way. Suited to newcomers who would like a calm, supportive start. Runs over twelve weeks with flexible weekly study.
- Python fundamentals from scratch — variables, functions, data structures
- Working with NumPy and pandas for data handling
- Introduction to scikit-learn and core ML concepts
- Final project: a small classification or regression model
- Mentor feedback on every submitted exercise
Practical Machine Learning Projects
A project-based course where you build, train, and assess models using real data, learning to interpret results with care. You progress through guided builds with mentor feedback and a small group for discussion. Suited to learners with basic Python who want grounded, practical experience. Runs over sixteen weeks at a comfortable weekly pace.
- Supervised and unsupervised learning in depth
- Feature engineering and model evaluation
- Introduction to neural networks with PyTorch
- Reading and interpreting results critically
- Final project: end-to-end ML pipeline on a real dataset
Full-Stack AI Development Track
A comprehensive programme covering model development, deployment, and working with current AI tools — centred on a portfolio project you can show with confidence. You receive regular mentor reviews and structured feedback, along with guidance on the skills the field values. Suited to dedicated learners aiming toward a professional role. Runs over twenty-four weeks with steady weekly study.
- Advanced PyTorch: custom architectures and training loops
- Working with LLM APIs and prompt engineering
- Model deployment: APIs, containers, cloud basics
- Building and documenting a production-grade portfolio project
- Mentor review sessions every two weeks
Which course is right for you?
Use this to get a rough sense. If you are still unsure, write to us — we will help you figure it out.
| Feature | Getting Started ฿7,200 |
Practical ML ฿11,000 |
Full-Stack AI ฿13,900 |
|---|---|---|---|
| Prior coding experience needed | None | Basic Python | Python + ML basics |
| Duration | 12 weeks | 16 weeks | 24 weeks |
| Weekly study hours | ~8 hrs | ~10 hrs | ~12 hrs |
| Named mentor | |||
| Group discussion sessions | |||
| Model deployment content | |||
| LLM API and prompt engineering | |||
| Payment in two parts |
Best for
Getting Started
Anyone curious about AI who has not written code before, or who tried and found existing resources too fast.
Best for
Practical ML
Someone who can write basic Python and wants to understand how machine learning actually works by building things.
Best for
Full-Stack AI Track
A person with ML experience who wants to work in a professional AI role and needs a portfolio to show alongside their knowledge.
Standards that apply across all courses
The same commitments hold regardless of which course you join.
Data privacy (PDPA-compliant)
Submitted work and personal data are stored securely and never shared with third parties. Learner records are kept only as long as needed.
Quarterly curriculum review
Course materials are reviewed and updated every quarter. When tools or practices change significantly, the exercises reflect that promptly.
Mentor-to-learner ratio
No mentor works with more than eight active learners at any time. This is a structural limit, not an aspiration.
Written feedback on all submissions
Every exercise you submit receives written comments specific to your work. No automated responses, no generic rubric scores.
Honest course descriptions
What is on the course page is what the course covers. We do not exaggerate scope or outcomes to attract enrolment.
Post-course material access
You keep access to course materials after completion. The field moves; being able to revisit reference content later has real value.
Course fees
All fees are in Thai baht. Payment arrangements are available for the two longer courses — contact us to set one up.
- All course materials
- Named mentor
- Group sessions
- Final project review
- Post-course material access
- All course materials
- Named mentor
- Group sessions
- Final project review
- Two-part payment option
- All course materials
- Named mentor + biweekly reviews
- Group sessions
- Portfolio project + documentation
- Two-part payment option
Unsure where to begin?
Tell us about your background and what you are hoping to do. We will let you know which course makes most sense for where you are now.
Get in Touch