Three Courses, One Clear Path
From Python fundamentals to a fully mentored applied project — each Tensora course builds on the last, so there's no wasted overlap and no unexplained gaps.
Back to HomeHow the Curriculum Is Structured
Each course is broken into modules, and each module has a defined scope. Before you begin, you can read a plain description of what that module covers and what you'll submit at the end of it. There are no surprise topics and no filler content added to extend apparent length.
Materials are built around exercises rather than passive reading. You write code, handle data, and make decisions — then submit your work for instructor review. The feedback you receive is specific to what you submitted, which means it's actually useful for learning.
The three courses are designed to be taken in sequence, though the first two can also stand alone if your goals don't require the full path.
Module opens with defined scope
You know exactly what skills the module addresses before you begin
Work through guided exercises
Code-based tasks that build the skill described in the module scope
Submit work for instructor review
Submissions are read and responded to within the weekly feedback cycle
Apply feedback and progress
Instructor notes help you refine your understanding before the next module
AI Programming Essentials
Beginner · ~10 weeks part-time · Weekly feedback
A starter course in Python, data handling, and the basics of how learning systems work, taught through guided exercises. Built for newcomers. The course runs over roughly ten weeks at a part-time pace, with weekly feedback on submitted work. A course completion record is issued on finishing.
What You'll Cover
- Python syntax, data types, and control flow
- Working with lists, dictionaries, and functions
- Introduction to data handling with pandas
- How supervised learning algorithms work in plain terms
- Simple model training and evaluation walkthrough
Process Steps
- 01Set up your Python environment and complete the orientation module
- 02Work through weekly coding exercises and submit for review
- 03Apply instructor feedback and build on each week's work
- 04Complete the final module and receive your course completion record
one-time fee, all materials included
Best for
People who want to understand how AI and machine learning work from the inside, and who are starting from little or no programming background. Also suitable for those returning to Python after a gap.
Hands-On Machine Learning
Intermediate · Project-based · Peer review included
An intermediate course on preparing data and training, testing, and improving models responsibly. For learners with some coding background seeking practical work. Includes mini-projects and peer review sessions with other course participants.
What You'll Cover
- Data cleaning, missing values, and feature engineering
- Training and evaluating classification and regression models
- Cross-validation and avoiding common pitfalls
- Model iteration and responsible use of ML pipelines
- Mini-projects using datasets from practical domains
Process Steps
- 01Review prerequisites and complete the course intake exercise
- 02Work through data handling and model training modules
- 03Complete mini-projects and participate in peer review
- 04Submit final project with instructor review and written feedback
one-time fee, all materials included
Best for
Learners who have completed the first course, or who already write Python comfortably and want to move into practical ML work — data preparation, model selection, and responsible iteration.
Applied AI Mentorship
Advanced · Mentored programme · Portfolio project
A mentored programme supporting learners through a full applied project, with planning, regular feedback, and a final showcase. Suited to those building a portfolio piece. Includes scheduled mentoring sessions and access to a cohort community.
What You'll Cover
- Project scoping and planning with your mentor
- Data sourcing, preparation, and pipeline design
- Model development, iteration, and evaluation
- Documenting your decisions and methodology
- Final showcase preparation and review
Process Steps
- 01Initial session with your mentor to scope and plan the project
- 02Regular scheduled check-ins as you build and iterate
- 03Participate in the cohort community for peer perspective
- 04Complete the final showcase with documented methodology
one-time fee, mentoring sessions included
Best for
Learners who have completed Hands-On Machine Learning and want to work on a full project with the support of a mentor — particularly those building a portfolio or transitioning into a role where AI skills are relevant.
Which Course Suits You?
| Feature | AI Programming Essentials |
Hands-On Machine Learning |
Applied AI Mentorship |
|---|---|---|---|
| Requires prior coding | No | Some Python helpful | Yes — Course 02 level |
| Instructor feedback | |||
| Peer review | |||
| Mentoring sessions | |||
| Portfolio project | |||
| Completion record | |||
| Price | ฿3,600 | ฿14,500 | ฿31,000 |
Not sure which to start with? Get in touch — we'll help you figure out the right starting point.
How We Maintain Quality Across All Courses
Data Privacy
Learner data is stored and handled in accordance with applicable regulations. We don't use your information for marketing purposes without your explicit agreement.
Consistent Review Standards
Instructors follow a consistent review framework so feedback quality doesn't vary between cohorts. Updates to this framework are communicated to learners.
Regular Content Review
Course materials are reviewed twice a year. When libraries or tools we teach change in ways that affect the exercises, materials are updated before the next cohort.
Manageable Cohorts
Cohort sizes are kept within limits so each learner's work gets proper attention. If a cohort is full, we'll let you know and offer the next available intake.
Responsive Communication
Office-hours emails are responded to within one business day. Phone support is available Monday to Saturday during posted hours.
Transparent Course Descriptions
Each course page describes what's included before you commit. There are no undisclosed fees and no features that turn out to require upgrades.
Course Fees
All fees are in Thai Baht (฿). Each course is a one-time payment — no subscription, no separate add-on charges.
Course 01
AI Programming Essentials
฿3,600
- All course materials
- Weekly instructor feedback
- Email support
- Completion record
Course 02
Hands-On Machine Learning
฿14,500
- All course materials
- Instructor feedback
- Peer review sessions
- Mini-projects
- Completion record
Course 03
Applied AI Mentorship
฿31,000
- All course materials
- Scheduled mentoring sessions
- Cohort community
- Portfolio project support
- Final showcase
Not Sure Where to Begin?
Send us a message describing your background and what you'd like to be able to do. We'll point you to the most sensible starting point.
Contact Us