Three courses, each with a clear scope and a set price
From a first data notebook to a deep learning capstone. All self-paced, all online, all priced in Thai Baht.
Back to HomeHow the courses are structured
Each Pythonia course follows the same basic structure: recorded lessons delivered week by week, notebook exercises that run on your own machine, and at least one form of direct feedback from an instructor or mentor. The level of support increases with the length and price of the course — the Bootcamp includes significantly more instructor contact than the introductory course, which reflects the commitment required from both sides.
We designed the courses to be taken in order if you are starting from scratch, but they can also be entered at the right level if you already have relevant experience. The background requirements for each course are clearly stated, and we will help you assess your starting point if you write to us.
Watch when it suits you. Lessons are structured by week but have no fixed viewing time.
Each lesson connects to a notebook that runs on standard Python. No proprietary platform.
Written feedback on submitted notebooks. Not automated — a person reads your work.
Python for Data
A six-week online course for learners with a small amount of programming background who want to focus their Python on data work. The course covers tabular data handling, working with structured files, plotting, and producing a small notebook-style report. Each week includes recorded lessons, short notebook exercises, and a take-home practice set. Students receive written feedback on two submitted notebooks during the course. Suitable for learners able to dedicate three to five hours per week.
- Six weeks of recorded lessons and weekly practice sets
- Written feedback on two submitted notebooks
- Email support with next-business-day response
- All materials: no separate downloads to purchase
- 1Watch the week's recorded lesson (approx. 40–60 min)
- 2Work through the accompanying notebook exercise
- 3Complete the take-home practice set
- 4Submit notebook for feedback at weeks 3 and 6
Applied Machine Learning Pathway
A twelve-week pathway for learners who have a Python and data handling foundation. The pathway covers regression and classification, model evaluation, careful feature engineering, lightweight neural network basics, and the production of two small portfolio projects. Weekly recorded lessons are paired with practical notebooks and mentor office hours over video. Suitable for committed learners with roughly six to eight hours per week to spend.
- Twelve weeks of recorded lessons and practical notebooks
- Two portfolio projects — yours to keep
- Mentor office hours over video (weekly)
- Written notebook feedback at set milestones
- 1Watch recorded lesson and work through the paired notebook
- 2Attend the weekly mentor office hours session (optional but recommended)
- 3Work on the current portfolio project
- 4Submit portfolio notebooks at weeks 6 and 12 for written feedback
Deep Learning Bootcamp
A sixteen-week intensive bootcamp focused on practical deep learning for learners with prior programming experience. The bootcamp covers neural network fundamentals, training stability, common architectures for vision and language, deployment of a small model, and the production of a portfolio-ready capstone project. Weekly recorded lessons are paired with hands-on notebooks, two mentor reviews of student code, and a final capstone presentation reviewed by a panel of three educators.
- Sixteen weeks of recorded lessons and hands-on notebooks
- Two mentor code reviews of your work
- Portfolio-ready capstone project
- Final capstone reviewed by a panel of three educators
- Weekly mentor office hours
- 1Weeks 1–4: Neural network foundations and training setup
- 2Weeks 5–8: Vision and language architectures — first code review at week 8
- 3Weeks 9–12: Deployment and capstone scoping — second code review at week 12
- 4Weeks 13–16: Capstone development and final panel presentation
Choosing the right starting point
If you are unsure which course fits your background, this table should help. If you are still not sure after reading it, write to us.
| Feature | Python for Data ฿6,800 |
ML Pathway ฿18,800 |
Deep Learning ฿32,800 |
|---|---|---|---|
| Duration | 6 weeks | 12 weeks | 16 weeks |
| Hours per week | 3–5 hrs | 6–8 hrs | 8–10 hrs |
| Background required | Some coding | Python + data basics | Programming experience |
| Written notebook feedback | ×2 | at milestones | ×2 code reviews |
| Mentor office hours | Weekly | Weekly | |
| Portfolio projects | 2 projects | Capstone | |
| Panel capstone review | 3 educators | ||
| Best for | Data beginners | Building ML skills | Deep learning focus |
What applies across all three courses
Data Security
Student information is handled in accordance with our privacy policy. Notebook submissions are stored securely and not used for any purpose other than providing feedback.
Annual Content Review
Materials are reviewed and updated once a year. Library version changes and new practice guidance are incorporated before each new cohort.
Feedback Turnaround
Written notebook feedback is returned within five business days of submission. Office hour sessions are scheduled at fixed weekly times communicated at enrolment.
Open-Source Tools Only
No paid software or proprietary platforms at any point. Python, Jupyter, and the relevant open-source libraries are the only requirements.
Support Channel
Students can write to [email protected] at any point during their course. Response within one business day on weekdays.
Transparent Pricing
Each price covers everything described on the course page. There are no modules sold separately, no subscription fees, and no premium tier for features already listed.
Course fees at a glance
- 6 weeks
- 2× written feedback
- Email support
- All materials included
- 12 weeks
- Milestone feedback
- Weekly office hours
- 2 portfolio projects
- 16 weeks
- 2× code reviews
- Panel capstone review
- Capstone project
Ready to enquire or not sure where to start?
Write to us with your background and the course you are considering. We will confirm whether the fit is right before you commit to anything.
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