// course catalogue
Three Tracks, One Clear Path
Through AI Development
From writing your first data pipeline to deploying and monitoring a production system — each track is designed to take you from where you are to somewhere specific.
Back to Home// our methodology
How the Courses Are Structured
Every Tensora course follows the same core structure regardless of level. Material is organised into discrete notebook cells — each one covering a single concept with a short reading and a task that produces reviewable output. There are no passive video lectures. Every cell ends with something you submit.
This approach is deliberate. Learning to write code that does what you intend requires practice and feedback, not observation. The cell format keeps the focus on the work itself rather than on consuming explanations.
Exercises are reviewed by a human who reads your submission and writes specific feedback. On the structured tracks, a named mentor is also available for scheduled sessions where you can ask questions in depth.
Read the cell
A focused explanation of one concept, with context for why it matters and how it fits the broader topic.
Complete the task
A hands-on exercise that applies the concept. Tasks range from short code snippets to full data preparation pipelines depending on the track level.
Submit for review
Your work is submitted to a reviewer who reads it and sends written feedback within two working days on structured tracks.
Progress to the next cell
Once feedback is received and the task is satisfactory, you move to the next cell. The structure keeps the pace honest.
Track 01 · Self-paced · 8 weeks
AI Coding Fundamentals
An introductory course covering the programming and data basics that underpin machine learning. Material is taught through small hands-on tasks in Python. Suitable for learners who have seen some code before but have not yet worked with data or numerical libraries. Self-paced, with weekly live question sessions and reviewed exercises. Starter notebooks are included.
- Python environment setup and working practice
- Data structures for numerical work
- Reading, cleaning, and summarising datasets
- Introduction to key libraries (NumPy, pandas)
- Weekly question sessions included
Track 02 · Structured · 12 weeks · mentor feedback
Building Models: Applied Course
An intermediate course on preparing data and building and evaluating models on realistic datasets, with careful attention to honest results. For learners with foundations in place. Runs over twelve weeks with a named mentor who reviews submitted work and holds scheduled feedback sessions. Includes datasets, briefs, and step-by-step walkthroughs.
- Data preparation for modelling (handling missing values, encoding, scaling)
- Supervised learning methods in scikit-learn
- Model evaluation: metrics, cross-validation, and honest reporting
- Realistic datasets from regional industry contexts
- Mentor feedback on all submitted work
Track 03 · Structured · 16 weeks · portfolio project
Production AI Engineering Track
A comprehensive track on deploying and maintaining dependable AI systems, structured around a portfolio project that learners take from planning through to a working, documented system. For committed learners building toward independent work. Runs over sixteen weeks with mentor sessions and code reviews. Includes a project framework and a progress record you keep.
- Serving models via APIs and scheduled pipelines
- Monitoring, logging, and drift detection
- Version control and reproducibility practices
- Portfolio project with structured code reviews
- Progress record and completed project yours to keep
// which track?
Choosing the Right Starting Point
The table below compares what each track includes. If you are unsure where your background places you, send us a message and we will help you choose.
| Feature | Fundamentals ฿4,100 |
Applied ฿16,200 |
Production ฿34,500 |
|---|---|---|---|
| Duration | 8 weeks | 12 weeks | 16 weeks |
| Self-paced | |||
| Weekly Q&A sessions | |||
| Written exercise feedback | |||
| Mentor sessions included | |||
| Code review sessions | |||
| Portfolio project | |||
| Deployment & monitoring | |||
| Best for | Learners new to data work and Python | Learners ready to work with real datasets | Learners building toward independent AI projects |
// standards across all tracks
Protocols That Apply to Every Course
Data Privacy
Learner data and submitted work is stored securely. Nothing is shared with third parties for advertising. PDPA compliant.
Regular Curriculum Review
All notebooks and library references are reviewed twice per year and updated before each new cohort starts.
Two Working-Day Response
Exercise submissions on structured tracks receive written feedback within two working days. Questions receive a reply within the same timeframe.
Clear Enrolment Terms
What is included, what is assumed, and what the course delivers are stated plainly before any payment is made.
Standard Environments
Notebooks run in Python environments that do not require special proprietary software or paid cloud accounts to complete exercises.
Materials Ownership
All notebooks, datasets, and submitted work belong to the learner. Access does not expire at the end of a course.
// pricing
Course Fees
Track 01
AI Coding Fundamentals
฿4,100
one-time · self-paced
- 8-week curriculum
- Starter notebooks
- Weekly Q&A
- Exercise reviews
Track 02
Building Models
฿16,200
one-time · 12 weeks
- 12-week curriculum
- Datasets & briefs
- Mentor feedback
- Scheduled sessions
Track 03 · Recommended
Production Engineering
฿34,500
one-time · 16 weeks
- 16-week curriculum
- Portfolio project
- Code review sessions
- Progress record
Payment arrangements available for the longer tracks. Contact us to discuss options before enrolling.
// get started
Not Sure Which Track to Start With?
Send a message with a few lines about your background and what you are hoping to learn. We will recommend a starting point and answer questions about the curriculum.
Contact Us