Three Courses,
One Coherent Path into AI
From foundational maths and Python to responsible scaling and a capstone project — each course builds directly on the previous one.
← Back to HomeHow the Curriculum Is Structured
Tensora's three courses follow a deliberate order. Course 01 establishes the mathematical and programming foundations that everything else depends on. Course 02 applies those foundations to model building with real data. Course 03 extends that into larger systems and concludes with a capstone project.
Each course uses a combination of reading, worked examples, exercises, and project submissions reviewed by a mentor. The pace is self-directed within each course's timeline, so you are not locked to a weekly lecture schedule.
Read and study the material
Each section contains readings, annotated examples, and exercises to work through before moving on.
Complete the project work
Projects apply what you have studied. You write both the code and a plain-language explanation of your approach.
Receive mentor feedback
A mentor reads your submission and returns specific written comments, typically within five working days.
Revise if needed and move forward
If revisions are needed, you address the comments before proceeding. If not, you move on to the next section.
Maths & Code Essentials
฿3,400
A foundation track pairing the core mathematics behind machine learning with the Python to put it into practice, taught gently with worked examples. Built for steady beginners. Learners finish able to follow and implement basic model ideas.
What you will cover
- Linear algebra: vectors, matrices, and operations used in ML
- Probability and statistics essentials
- Python fundamentals and the NumPy/pandas basics
- Reading and interpreting a simple model
- Two mentor-reviewed project submissions
Typical pace
8–10 weeks at roughly 8–10 hours per week. Self-directed within this window.
Enquire About This CourseModel Building Workshop
฿6,500
An intermediate track focused on building and tuning models through repeated, hands-on practice with real data. Covers honest evaluation and clear write-ups. Completed with several mentor-reviewed projects.
What you will cover
- Supervised learning: regression and classification models
- Working with real, messy datasets
- Model evaluation: what the metrics actually mean
- Hyperparameter tuning without overfitting
- Four mentor-reviewed project submissions
Typical pace
10–12 weeks at roughly 10 hours per week. Prerequisites: Course 01 or equivalent background.
Enquire About This Course
Scaling & Capstone
฿10,900
A senior track on working with larger datasets and models responsibly, finishing with a self-directed capstone. Emphasises good engineering and documentation. Concludes with a presented project for the portfolio.
What you will cover
- Data pipelines and larger-scale processing
- Introduction to neural networks and deep learning concepts
- Responsible deployment considerations
- Self-directed capstone project with mentor guidance
- Final presentation reviewed for portfolio use
Typical pace
12–16 weeks including capstone. Cloud compute environment included. Prerequisites: Course 02 or equivalent.
Enquire About This CourseHelp Choosing the Right Level
Use this table to see which course fits where you are right now. If you are unsure, contact us and we will help you work it out.
| Feature | Course 01 ฿3,400 |
Course 02 ฿6,500 |
Course 03 ฿10,900 |
|---|---|---|---|
| Best for beginners with no Python | — | — | |
| Requires prior Python knowledge | — | ||
| Works with real datasets | — | ||
| Cloud compute environment included | — | — | |
| Portfolio capstone project | — | — | |
| Mentor-reviewed project submissions |
Shared Across All Three Courses
Human Mentor Review
Every project submission is reviewed by a person, not an automated system. Response within five working days.
Documentation Standards
Each project includes a written explanation. Mentors evaluate both the technical work and how clearly you describe your approach.
Data Privacy
Learner data is handled according to our privacy policy. We collect only what is necessary and do not share it with third parties for marketing.
Responsible AI Coverage
Model limitations, bias considerations, and responsible deployment are covered throughout — not saved for a footnote at the end.
Materials Updated Regularly
Course content is reviewed by the teaching team and updated when relevant changes in tools or practices occur in the field.
Transparent Terms
Pricing, cancellation conditions, and what each course includes are stated clearly before enrolment. No hidden conditions.
Course Fees in Thai Baht
All prices include materials, exercises, and mentor review. Cloud compute is included in Course 03.
Maths & Code Essentials
- Full course materials
- 2 mentor-reviewed projects
- Email support
- 8–10 week timeline
Model Building Workshop
- Full course materials
- 4 mentor-reviewed projects
- Real dataset exercises
- 10–12 week timeline
Scaling & Capstone
- Full course materials
- Cloud compute included
- Capstone + portfolio project
- 12–16 week timeline
We Will Help You Find the Right Course
Send us a message with a brief note about your background and what you are hoping to build. We will suggest the most suitable starting point.
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