Our Story
Knowledge That Builds,
Piece by Piece
Sinar Code was set up in Petaling Jaya to give Malaysian learners a clear, calm path through AI development — without the noise and without skipping steps.
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How Sinar Code Came Together
Sinar Code started from a straightforward observation: many people in Malaysia wanted to learn AI and machine learning, but the available resources were either too fragmented, too rushed, or assumed a level of background that most learners simply did not have.
In 2022, a small group of developers and educators based in Petaling Jaya decided to build something different. Rather than compressing everything into dense bootcamps or scattering learners across dozens of disconnected tutorials, they designed a curriculum that works like a mosaic — each concept is a tile, and the tiles are laid out in an order that makes sense.
The school opened its first cohort in early 2023 with just eight participants. Feedback from those learners shaped the second cohort, which shaped the third. The format has been refined steadily, but the core principle has not changed: no learner should feel lost, and no idea should be introduced before its neighbours are in place.
Today Sinar Code runs three tracks — AI Foundations, Machine Learning, and Deep Learning — serving professionals, career changers, and developers who want to add AI skills to their existing work. The team remains small by intention, so the personal quality of instruction stays consistent.
Our Mission
To make AI development education feel orderly and approachable — a process where each new idea connects clearly to what came before it.
Our Vision
A Malaysia where developers at all levels can add AI skills to their work without having to navigate confusing, misaligned, or overpromising resources.
Our Values
- Clarity over speed
- Honest feedback, given with care
- Small groups, not mass enrolments
- Curriculum built from learner feedback
The Team
The People Behind the Courses
Ahmad Razif
Founder & Lead Instructor
Ahmad has been working with machine learning systems since 2016 and developed the mosaic curriculum framework that underpins all three tracks.
Li Shan
Machine Learning Instructor
Li Shan teaches the Machine Learning Track and brings hands-on project experience from her background in fintech data systems across the Klang Valley.
Priya Krishnan
Deep Learning Specialist
Priya leads the Deep Learning Mosaic and has deployed neural network systems in both Malaysian and regional commercial contexts.
Standards
How We Maintain Quality
Reviewed Curriculum
Every module is reviewed after each cohort. Sections that cause consistent confusion are rewritten, not just annotated.
Written Instructor Feedback
All submitted project work receives written notes, not just scores. Feedback is specific to the learner's approach, not generic.
Data Privacy
Learner data is handled in line with Malaysian personal data regulations and is never shared with external parties for advertising.
Cohort Size Limits
Each intake is capped to keep instructor attention available. We will not open additional spots simply to fill revenue targets.
Session Recordings
All live sessions are recorded and available to enrolled learners for the duration of their track, so missed classes do not mean missed content.
Completion Certificate
Certificates are issued only after project submission and instructor sign-off — they represent actual capability, not just attendance.
Our Approach
AI Education Designed for Malaysian Learners
The field of AI and machine learning has moved quickly over recent years, and the volume of learning material available has grown with it. What has not kept pace, in many cases, is organisation. A learner who opens three well-regarded online resources on the same topic will often encounter three different entry points, three sets of assumed knowledge, and three ways of describing the same underlying concept.
Sinar Code was designed with that problem in mind. The mosaic framework that underlies every track starts with the observation that knowledge sticks when it is connected. A concept introduced in isolation — without the neighbouring ideas already in place — tends to feel arbitrary, and arbitrary things are difficult to retain and apply.
Each track is built from the ground up with connection in mind. Python syntax is introduced with data handling already in view. Data handling is framed in terms of the model problems it serves. Model concepts are presented in the context of real tasks. The learner is never asked to hold something in memory without understanding why it matters yet.
Being based in Petaling Jaya is not incidental to how the school operates. Malaysian pricing, Malaysian business hours, an understanding of the local tech and business landscape, and the ability to have a direct conversation with someone who will actually teach your course — these are things that matter to learners and that a remote or fully automated offering cannot replicate.
The team at Sinar Code looks forward to being part of wherever your AI learning takes you next.
Ready to Find Your Track?
Send a message and we will help you work out where to begin — no pressure, just a straightforward conversation.
Get in Touch