About Codeloom
A school shaped around how learning actually holds together
We started Codeloom because we believed AI education could be more deliberate — structured for real progress rather than surface-level speed.
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Built in Chiang Mai, shaped by experience
Codeloom was founded in 2021 by a small group of AI practitioners and educators who kept running into the same problem: people wanted to learn AI seriously, but most courses were either too shallow or too isolated from how real work actually gets done. Chiang Mai's growing technology community, along with its international learner base, made it a natural home for something more considered.
The name refers to something we think about a lot. A loom brings separate threads into something whole and durable. That's what a well-designed curriculum does — it takes isolated skills and weaves them into knowledge that holds up under pressure. Our courses are sequenced with that in mind: each one prepares you for the next, and the practice you build at each stage carries forward.
We keep cohorts small on purpose. Fewer students means more genuine feedback, more room for questions that don't fit neatly into a lesson plan, and more space to work at the pace the material actually demands.
Mission
To make structured AI education available to anyone who approaches it with patience and curiosity — regardless of prior background, location, or pace.
Vision
A region where AI skills are built through genuine understanding rather than surface familiarity — where learners can do the work, not just describe it.
Approach
Small cohorts, mentor-led feedback, and project work that mimics what practitioners actually do. No shortcuts, no noise — just steady, supported practice.
The Team
People behind the curriculum
Practitioners first, educators second — each member of the team has worked in the field before moving into teaching.
Nathapong Teerasak
Curriculum Lead
Eight years building ML pipelines in fintech before joining Codeloom. Nathapong designs the course architecture and runs the Applied AI Engineering Program mentor sessions.
Siriporn Monthon
Python & Foundations Mentor
Former data analyst and self-taught Python developer who now leads the Intro course. Siriporn remembers what it felt like to start from nothing, and teaches accordingly.
Kasem Wiriyarat
Deep Learning Instructor
Kasem spent six years at a Bangkok AI research lab before moving to Chiang Mai. He leads the Deep Learning Workshop and specialises in making neural networks feel intuitive rather than mysterious.
How We Work
Standards we hold ourselves to
These aren't policies for the sake of it — they reflect what we've learned about what makes technical education actually land.
Human feedback, not automated scoring
Every assignment gets reviewed by a mentor. We don't outsource feedback to algorithms because context matters — especially early on.
Data and privacy handling
We use learner data only to improve the learning experience. We don't sell or share personal information with third parties for marketing purposes.
Curriculum reviewed each cohort
AI moves fast. We revise content between cohorts, not annually — keeping exercises and frameworks current with what practitioners are actually using.
Small cohort sizes
We cap each cohort to maintain a ratio where mentors can give each learner individual attention. When we're full, we're full — no exceptions.
Honest about what we cover
Course pages say what's included and what isn't. We don't oversell scope or imply outcomes we can't meaningfully support.
Accessible support channels
Questions get answered by the teaching team, not a ticketing system. Learners can reach someone who understands the course material.
Depth & Context
What shapes how we teach AI
Teaching AI development well requires more than delivering accurate technical content. It requires understanding the order in which concepts should appear, the projects that make ideas feel concrete, and the rhythm of feedback that keeps learners engaged without overwhelming them. Codeloom was built with that sequence as a starting point, not an afterthought.
Our instructors have each spent years in applied roles — building production pipelines, reviewing code in team environments, and working with the frameworks that practitioners rely on in 2024 and 2025. When they teach, they draw on decisions they've actually made, not textbook examples constructed to illustrate a point neatly.
The Chiang Mai context matters to us as well. Thailand's technology sector is developing quickly, and there's growing demand for engineers and analysts who understand how to work with machine learning tools at a practical level. We designed our programme to serve learners in the region, and to connect with the broader Southeast Asian technology community.
Learning AI is not something that happens in a weekend. The pace of Codeloom's courses reflects that: eight weeks for a thorough introduction, twelve for deep learning practice, six months for engineering-level work. Those durations are based on what it actually takes to develop durable understanding, not on making the programme look accessible by shortening it.
Join the next cohort
Curious about where to start?
We're happy to talk through which course makes sense for your background and goals — no commitment, just a conversation.
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