Our Story
Education built around how AI development actually works
Codeloom started from a straightforward observation: most AI courses skip the parts that matter most — the habit of careful engineering, the ability to evaluate what you've built, and the knowledge to explain your choices.
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About Codeloom
Codeloom was founded in Bangkok with a specific purpose: to give learners in Thailand and across Southeast Asia access to structured AI development education that doesn't cut corners on the engineering fundamentals.
The name reflects our approach. A loom is a structure — not the fabric itself, but the framework through which threads are woven into something useful and durable. That's what we try to provide: a framework for learning, not just a collection of topics to skim through.
We are not a platform with thousands of courses. We run three carefully scoped tracks, each built to connect directly to the next. Learners who complete Track 01 are ready for Track 02. Learners who finish Track 02 have the foundation to work through the Capstone.
Every piece of course content, every exercise, and every project is reviewed by the same people who teach here — working practitioners who use machine learning in their own professional lives and can tell the difference between a learner who understands what they're doing and one who has memorised the steps.
Our Mission
To provide clear, well-structured AI development education that takes learners from their first encounter with code to a point where they can build, assess, and discuss working models — at a pace that allows real understanding to develop.
Our Approach
Linear curriculum design with deliberate pacing. Projects that reflect real engineering decisions rather than pre-solved exercises. Feedback from people who work with AI professionally, not automated graders or generic rubrics.
Where We Work
Our office and administrative team are based in Sathon, Bangkok. All three course tracks are delivered entirely online, so learners join from Thailand and elsewhere without restriction.
The Team
People behind the courses
Nattakit Krongkaew
Curriculum Lead & Senior Instructor
Nattakit designs the track structure and oversees all course content. He has spent the past eight years working with data systems across finance and logistics in Southeast Asia, and brings that operational perspective into how exercises are framed.
Siriporn Thongchai
Mentorship Coordinator
Siriporn coordinates the mentorship programme for Track 02 and the Capstone track, matching learners with practitioners suited to their project direction. She previously led training and development roles at two Bangkok-based technology firms.
Arjun Thampy
Technical Reviewer & Practitioner Mentor
Arjun conducts code reviews and capstone mentorship sessions. He works as a machine learning engineer across project engagements in Thailand and India, and contributes to Codeloom's curriculum review process on a part-time basis.
Our Standards
How we maintain course quality
Curriculum review cycle
Course content is reviewed at minimum every six months against developments in AI tooling and methodology. Topics that become outdated are rewritten, not left in place.
Practitioner-conducted code review
Project submissions in Track 02 and Track 03 are reviewed by practitioners, not assistants or automated tools. Feedback references specific decisions in the submitted code.
Data privacy handling
Learner data is stored and handled in accordance with applicable Thai data protection requirements. We collect only what is needed to run courses and respond to enquiries.
Exercise validation
All exercises are tested by team members before inclusion in a track. Exercises that produce confusion disproportionate to their learning value are revised or removed.
Learner feedback process
Each track includes structured feedback points where learners can flag what is unclear or where the pacing feels off. That feedback feeds directly into curriculum adjustments.
Transparent enrolment
Pricing, curriculum scope, and what each track includes are published openly. We don't use upsell sequences or obscure what a course covers before someone commits to it.
Expertise & Values
What we care about in AI education
Machine learning education has expanded rapidly, but a large part of it focusses on tool familiarity rather than understanding. Learners emerge able to run a notebook but uncertain about what the output actually means or whether they've approached the problem sensibly. Codeloom's curriculum is built around addressing that gap specifically.
We prioritise evaluation — the ability to look at what a model does and form a considered view of whether it is working, why, and what the limitations are. This is less common in course design than it should be, and it matters more as AI systems are applied to real decisions.
We also care about honesty in how courses are described. AI development takes time to learn well. There are no reliable shortcuts through the fundamentals, and a course that implies otherwise is doing its learners a disservice. Our track structure reflects realistic learning timelines and doesn't make claims about what finishing a course will produce for someone's career or income.
The Codeloom approach draws on the node-and-layer logic of neural network diagrams as a design principle for curriculum: each concept connects deliberately to what came before and prepares the ground for what follows. That structure makes it easier for learners to know where they are and what the next step asks of them.
We are based in Bangkok and our work is oriented toward the Southeast Asian AI development community, though the courses attract learners from much further afield. The English-language instruction reflects the reality that technical AI work operates largely in English, and we think it serves learners well to work within that context from the beginning.
Take the next step
Want to know more before deciding?
Send a question through the contact form. We're happy to explain what each track involves, how the feedback process works, or which course is a reasonable place to begin given your background.
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