Three Programs,
One Steady Path
From your first Python script to deploying a working AI system — each program is self-contained, mentor-supported, and paced to suit your schedule.
← Back to HomeHow the Programs Are Built
Each program follows the same structural pattern: concise reading material, a tidy code panel demonstrating the concept, a practice checklist with plainly worded exercises, and instructor review of your submitted work. You move between modules at your own pace, and earlier materials remain open throughout.
Read
Concise module material with annotated examples
Review Code
Study the worked example in the code panel
Complete Checklist
Work through the plain-language exercise list
Receive Feedback
Instructor reviews submission within two days
Introduction to AI Programming
A calm introductory program covering programming fundamentals and the building blocks of machine learning through small practical tasks. Built for newcomers who value a structured, unhurried start, with materials to revisit freely. Includes guided practice and supportive feedback.
- No prior experience required — starts from the basics
- Python syntax, data types, and control flow
- Introduction to NumPy and basic data manipulation
- First supervised learning examples using scikit-learn
- All materials revisitable throughout enrolment
Program Steps
Python environment setup and first scripts
Core data structures: lists, dicts, arrays
Functions, loops, and working with files
Introduction to machine learning concepts
First model: classification with scikit-learn
Best suited for:
People new to programming who want a solid, unhurried start with machine learning concepts introduced gradually.
Best suited for:
Learners with some programming background who are ready to work with real datasets and build their first meaningful models.
Data and Models Program
An intermediate program focused on preparing data and building models, worked through step by step with mentor support. We emphasise sound understanding over quick results. Suited to learners ready to deepen their practice on a comfortable schedule.
- Data loading, cleaning, and exploratory analysis
- Feature engineering and preprocessing pipelines
- Regression and classification models in depth
- Model evaluation and cross-validation methods
- Introduction to neural network structure
Program Steps
Data sourcing and pandas exploration
Cleaning messy data and building pipelines
Selecting and training appropriate models
Evaluating and improving model performance
Introduction to deep learning fundamentals
AI Systems Engineering Track
A comprehensive program covering model development, evaluation, and deployment practices, concluding with a guided project. We keep the pace measured and encouraging. Well suited to learners building toward professional-level skills with care and patience.
- Advanced model architectures with PyTorch
- Experiment tracking with MLflow
- REST API deployment using FastAPI
- Model monitoring and versioning practices
- Guided capstone project from design to deployment
Program Steps
Advanced model design and training workflows
Experiment tracking and reproducibility
Building and testing a model API
Deployment, monitoring, and versioning
Guided capstone: end-to-end AI system
Best suited for:
Learners who have completed introductory and intermediate AI work and want to build toward a professional level of practice with patience and structure.
Choosing the Right Program
If you are unsure which program suits your current level, this table may help. You can also contact us and we will talk it through.
| What you will cover | Intro ฿3,500 |
Data & Models ฿5,800 |
Engineering ฿7,800 |
|---|---|---|---|
| Python programming fundamentals | Prior knowledge assumed | Prior knowledge assumed | |
| ML concepts introduction | |||
| Data preparation pipelines | — | ||
| Model evaluation and tuning | — | ||
| PyTorch deep learning | — | — | |
| API deployment with FastAPI | — | — | |
| Experiment tracking (MLflow) | — | — | |
| Capstone guided project | — | — | |
| Mentor-reviewed exercises |
Technical and Operational Standards
Data Privacy — PDPA
Learner data is handled in accordance with Thailand's Personal Data Protection Act. No data shared for marketing purposes.
Current Tool Versions
Documented Python and library versions per program. Updated at least annually. Setup guides included to minimise environment friction.
Feedback Within 2 Days
Exercise submissions receive written instructor feedback within two Bangkok working days. Response times are tracked and maintained.
Cohort Size Limits
Active enrolment per instructor is capped to maintain feedback quality. New places open as previous cohorts progress.
Content Review Cycle
All program material is reviewed by the technical team at minimum twice yearly. Outdated examples are updated or replaced.
Direct Instructor Contact
Questions go to the instructors who wrote and teach the material, not a general support queue. Responses are specific and considered.
Program Fees
One fee covers all materials, exercises, and mentor review. No additional charges.
Introduction to AI Programming
฿3,500
One-time fee · all-inclusive
- Full module access
- Practice checklists
- Instructor feedback
- Revisit freely
Data and Models Program
฿5,800
One-time fee · all-inclusive
- Full module access
- Practice checklists
- Instructor feedback
- Real dataset exercises
AI Systems Engineering Track
฿7,800
One-time fee · all-inclusive
- Full module access
- Practice checklists
- Instructor feedback
- Guided capstone project
Not Sure Where to Start?
Drop us a message and describe your background briefly. We will suggest the most suitable starting point without any pressure.
Get in Touch