Three programmes, three positions in your learning network
Each programme at Naga Tech occupies a distinct place in the AI learning graph. Below you will find full details of what each one involves, how it is structured, and what it costs.
Back to HomeHow each programme is run
All three Naga Tech programmes share a common approach: material is structured into units, each unit ends with an applied exercise, and every submitted exercise receives a personal written response from an instructor. This is the central mechanism of all our teaching — the feedback loop that distinguishes study from passive consumption of content.
Beyond this shared foundation, each programme has a structure appropriate to its level and purpose. The Python course is sequential and foundational. The Computer Vision Specialisation is extended and research-informed. The mentorship is responsive to the individual project rather than a fixed curriculum.
Wherever a learner is in the network — at the foundation, the specialisation, or working on independent project work — the quality of attention they receive is the same.
Unit-based structure
Each programme is divided into self-contained units that build sequentially toward a defined body of knowledge.
Written feedback loop
All exercises are reviewed by a named instructor who responds in writing within five working days.
Real tools from day one
Students work in Jupyter-based environments with the libraries and frameworks currently used in the field.
Quarterly curriculum review
All programmes are updated each quarter; active students receive revised material without additional cost.
Python for Data and AI
A foundational course covering Python as it is used in data work and AI development, with attention to the libraries that have become standard in the field. The material moves at a deliberate pace, beginning with language fundamentals and building toward practical work with data manipulation, scientific computing, and visualisation. Each unit closes with applied exercises that the student completes and submits for written feedback. Suitable for learners new to the field or returning after some time away from active programming work.
What the programme covers
- Python language fundamentals — types, control flow, functions, and modules
- NumPy for numerical computation and array operations
- pandas for data loading, cleaning, and transformation
- matplotlib and basic visualisation for data analysis
- Introductory machine learning concepts using scikit-learn
How it works — step by step
Enrol and receive access — on enrolment you receive access to unit materials and the submission system.
Work through each unit — read the material, work through the examples, and complete the exercise at the end.
Submit your exercise — submit your work through the platform and receive written feedback within five working days.
Revise and move on — review the feedback, revise if needed, and progress to the next unit.
Receive completion statement — on finishing all units, a written completion statement is issued.
Computer Vision Specialisation
A specialised programme focused on computer vision, covering the foundational image processing concepts, the architecture of convolutional networks, and the contemporary methods used in image classification, object detection, and segmentation. The programme combines structured lessons with applied projects using established datasets and frameworks. Students work through the material over an extended period that allows time for genuine engagement with the source papers as well as the practical implementation work.
What the programme covers
- Image processing fundamentals — filters, transformations, and feature extraction
- Convolutional neural network architecture — theory and implementation in PyTorch
- Image classification, object detection, and segmentation methods
- Reading and engaging with current computer vision research papers
- Applied projects using established benchmark datasets
How it works — step by step
Prerequisites confirmed — applicants should have Python proficiency at the level of our foundation course or equivalent.
Theory and reading units — structured units combine conceptual material with scheduled source paper reading.
Implementation projects — applied project units require you to implement methods from scratch and submit for review.
Feedback at each stage — written instructor feedback is provided on both implementation work and paper reading responses.
Final project and completion — the programme closes with an independent project and issuance of a completion statement.
AI Project Mentorship
A mentorship engagement for learners working on a substantial AI project of their own choosing, whether for portfolio, study, or workplace application. The engagement includes scheduled review meetings with an experienced practitioner, written feedback on submitted work, and guidance on the technical and structural decisions that arise during the project. Suitable for learners who have completed foundational study and would value the careful attention of a mentor through the development of a meaningful piece of independent work.
What the mentorship includes
- Initial scoping meeting to clarify project goals and feasibility
- Scheduled review sessions at agreed intervals (typically fortnightly)
- Written feedback on all submitted work between sessions
- Guidance on technical decisions — architecture, data handling, evaluation
- Structural advice on project presentation and documentation
How the engagement runs
Application and matching — you describe your project and background; we match you with an appropriate practitioner mentor.
Scoping session — an initial meeting to clarify the project scope, key decisions, and the engagement structure.
Active development phase — you build; your mentor reviews submitted work and provides written feedback between sessions.
Review meetings — fortnightly video sessions to discuss progress, decisions, and next steps in depth.
Closing and documentation — the engagement closes with a final review and written feedback on the completed project.
Which programme fits you?
Use this table to identify the programme that matches your current level and goals.
| Feature | Python for Data ฿2,800 |
Computer Vision ฿4,800 |
AI Mentorship ฿8,200 |
|---|---|---|---|
| Prior Python needed | |||
| Written instructor feedback | |||
| Research paper reading | — | ||
| One-to-one video sessions | |||
| Fixed curriculum | |||
| Your own project focus | |||
| Completion statement | |||
| Best for | Beginners entering the field | Learners specialising in CV | Learners with a project ready |
Shared across all programmes
Data privacy
Student information is held only for the purposes of programme delivery. We comply with Thailand's Personal Data Protection Act (PDPA).
Instructor quality
All instructors and mentors are practitioners with documented field experience. No instructor teaches exclusively from academic background.
Response standards
Pre-enrolment queries answered within two working days. Submitted exercises reviewed and responded to within five working days.
Curriculum updates
Programmes are reviewed each quarter. Active students receive updated material reflecting current tools and methods without additional charge.
Completion documentation
All programmes issue a written completion statement upon finishing all requirements. Statements include programme title, duration, and instructor sign-off.
Early withdrawal policy
A full refund is available within seven days of enrolment, provided the programme has not yet commenced. Details are confirmed in writing before payment.
Clear pricing, no hidden tiers
Each programme price includes all material, instructor feedback, and quarterly updates for the duration of your enrolment.
Python for Data & AI
- All unit materials and exercises
- Written feedback on each submission
- Quarterly curriculum updates
- Written completion statement
- Email support from instructor
Computer Vision
- All unit materials and exercises
- Written feedback on each submission
- Research paper reading schedule
- Applied project datasets included
- Quarterly curriculum updates
- Written completion statement
AI Project Mentorship
- Initial scoping session
- Fortnightly video review meetings
- Written feedback on all submissions
- Technical and structural guidance
- Written completion statement
Not sure which programme to start with?
Write to us with your background and what you are trying to achieve — we will help you identify the right starting point without any pressure to commit before you are ready.
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