Three pathways, one continuous arc
From your first encounter with AI to building real generative applications — each course connects naturally to the next, without unnecessary repetition.
Back to HomeOur approach to AI education
Every course at Panya AI follows the same core pattern: short recorded lessons, practice tasks that build incrementally, regular check-in points with a mentor, and a final project that pulls everything together. The pacing is designed around a working adult who has 6–8 hours per week — not someone in full-time study.
Learn
Watch short lessons at your own schedule. Revisit any section as many times as you need.
Build
Complete practice tasks and guided projects that put concepts into working code.
Refine
Receive mentor feedback on your submissions and adjust before moving forward.
AI Foundations
A gentle introduction to the ideas behind modern AI — covering core concepts, simple Python practice, and how models learn from data. Suited to curious beginners and career changers. Runs over eight weeks at a relaxed pace, with recorded lessons, small practice tasks, and friendly feedback from mentors.
What you'll cover:
- What AI is and how it differs from traditional programming
- Python basics: data types, functions, and simple data handling
- How machine learning models are trained on data
- Common model types and when each is used
- Reading and interpreting model output responsibly
How the 8 weeks unfold:
Is this right for me? If you're starting from scratch or switching careers, this is where to begin. No coding background required.
Ask About This Course
Machine Learning Engineering
A practical course in building and training models, working through real datasets and clean coding habits. Suited to learners comfortable with basic Python who want to go further. Runs over twelve weeks with weekly check-ins, guided projects, code reviews, and supportive mentor sessions.
What you'll cover:
- Data preparation and exploratory analysis in Python
- Supervised learning: regression, classification, evaluation
- Model training, tuning, and preventing overfitting
- Clean code habits for ML projects
- Two guided projects using real-world datasets
How the 12 weeks unfold:
Is this right for me? If you've done the foundations course or already know basic Python, this is your next step.
Ask About This CourseApplied Generative AI
A hands-on course exploring language and image models, prompt design, and responsible use in real projects. Suited to those ready to build small applications. Runs over fourteen weeks with project milestones, a capstone project, peer collaboration, and thoughtful mentor guidance.
What you'll cover:
- How large language and image models work
- Prompt engineering for reliable, useful outputs
- Integrating APIs into small applications
- Responsible AI use, bias awareness, and output evaluation
- Capstone: a working application of your choice
How the 14 weeks unfold:
Is this right for me? If you've completed ML Engineering or have equivalent background, and you want to build with generative tools responsibly, this is your course.
Ask About This Course
Choosing the right course
| Feature | AI Foundations | ML Engineering | Applied GenAI |
|---|---|---|---|
| Duration | 8 weeks | 12 weeks | 14 weeks |
| Price | ฿1,560 | ฿2,940 | ฿4,160 |
| Prior coding needed | None | Basic Python | ML background |
| Mentor feedback | |||
| Code review | |||
| Peer collaboration | |||
| Capstone project |
Consistent standards throughout
Data privacy
Learner data used only to deliver your course. No third-party sharing for marketing purposes.
Updated content
Materials reviewed and refreshed each cohort to stay relevant to current AI tools and practices.
Ethical AI framing
Responsible use, bias, and limitations addressed in every course — not saved for the final week.
Flexible pacing
Pace adjustments handled individually through your mentor — no rigid cut-off penalties.
Not sure which course to start with?
Tell us a bit about your background and what you're hoping to build or understand. We'll suggest a starting point that makes sense for where you are now.
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