AI development curriculum overview

// Programmes

Three Programmes,
One Clear Path

From first principles to production deployment. Each programme is independent but connects directly to the next.

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// Methodology

How the Programmes Are Structured

Start from Code

Every programme begins with working code in the first week. Theory is introduced in the context of doing, not as a prerequisite.

Build, Review, Revise

Assignments are submitted, reviewed by a mentor, and returned with written comments. This cycle repeats throughout each programme.

Finish with Something Real

Each programme ends with a project that goes beyond the material — something you built, not just something you completed.

Foundations of AI Development
01

8 weeks · Self-paced

Foundations of AI Development

An introductory online course covering core programming for machine learning, data handling, and the basic concepts behind modern models, taught through worked examples and small projects. Suited to learners with some coding familiarity who want a solid grounding. Self-paced over eight weeks with weekly live question sessions and reviewed exercises. Includes course notes and starter project repositories.

  • Weekly live question sessions
  • Reviewed exercises with written comments
  • Course notes included
  • Starter project repositories provided

// How it works

1

Set up your Python environment using the provided starter repository

2

Work through weekly modules with worked examples and exercises

3

Attend or watch recordings of live Q&A sessions

4

Submit reviewed exercises and receive written feedback

5

Complete a small end-of-course project to bring the concepts together

฿4,000

one-time payment

Enquire
Applied Machine Learning Programme
02

12 weeks · Self-paced with cohort

Applied Machine Learning Programme

An intermediate programme that guides learners through building, training, and evaluating models on realistic datasets, with practical attention to data preparation and honest assessment of results. For those who have completed a foundations course or have equivalent experience. Runs over twelve weeks with mentor feedback on assignments. Includes datasets, project briefs, and recorded walkthroughs.

  • Mentor feedback on all assignments
  • Real datasets included
  • Project briefs with scope definition
  • Recorded walkthroughs archived

// How it works

1

Receive the first dataset and project brief

2

Work through data preparation and exploratory analysis

3

Build and train models, submit assignments for feedback

4

Evaluate results honestly using the methods covered in the programme

5

Complete a final project demonstrating the full ML workflow

฿16,000

one-time payment

Enquire
Advanced AI Engineering Track
03

16 weeks · With portfolio project

Advanced AI Engineering Track

A comprehensive track covering deployment, model evaluation, and the engineering practices behind reliable AI systems, structured around a substantial portfolio project. Designed for committed learners building toward serious independent work. Runs over sixteen weeks with regular mentor sessions and code reviews. Includes a project framework, review sessions, and a written progress record.

  • Regular mentor sessions and code reviews
  • Substantial portfolio project framework
  • Deployment and production engineering focus
  • Written progress record on completion

// How it works

1

Define your portfolio project scope with your assigned mentor

2

Build through the engineering modules — deployment, evaluation, monitoring

3

Submit code for review; receive written feedback at each stage

4

Complete and document your portfolio project

5

Receive your written progress record on completion

฿34,000

one-time payment

Enquire

// Decision Guide

Which Programme Is Right for You?

Feature Foundations Applied ML Advanced Track ★
Duration 8 weeks 12 weeks 16 weeks
Live Q&A sessions
Mentor assignment review
Code review
Real datasets provided
Portfolio project
Written progress record
Price (THB) ฿4,000 ฿16,000 ฿34,000

// Best for

Foundations

Developers with Python basics who want a solid introduction to how machine learning works in practice.

// Best for

Applied ML

Developers who've covered the basics and want to build and evaluate real models with mentored support.

// Best for

Advanced Track

Committed learners ready to work on deployment and engineering practices — and produce a substantial portfolio project.

// Standards

Across All Programmes

PDPA-Compliant Data Handling

Learner data is handled in line with Thailand's Personal Data Protection Act. We keep only what's needed to deliver the programme.

Written Feedback Standard

All mentored feedback is written, specific to your submission, and returned within 3–5 business days.

Curriculum Updated Each Cycle

Material is reviewed at the end of each programme cycle. The field changes and the content needs to keep up.

Responsive Support

Questions are answered during office hours, Monday–Friday 9–18. We aim to respond same-day where possible.

Recordings Always Available

All live sessions are recorded and accessible for the duration of your enrolment. Missing a session doesn't mean missing the content.

Starter Repositories Provided

Every programme provides GitHub-hosted starter repos so you're writing code in a realistic environment from day one.

// Pricing

Simple, Per-Programme Pricing

All prices in Thai Baht. One-time payment. No subscription.

Foundations

฿4,000

one-time · 8 weeks

  • Core ML programming
  • Weekly live Q&A
  • Reviewed exercises
  • Course notes & starter repos
Enquire

Applied ML

฿16,000

one-time · 12 weeks

  • Model building on real datasets
  • Mentor assignment feedback
  • Recorded walkthroughs
  • Project briefs included
Enquire

Advanced Track

฿34,000

one-time · 16 weeks

  • Deployment & production engineering
  • Mentor sessions & code reviews
  • Portfolio project framework
  • Written progress record
Enquire

// Start

Ready to Pick a Programme?

If you're not sure where to start, send us a message and we'll give you an honest view based on your background.

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