Education that treats you as the professional you are
Cendekia was built around a simple observation: working professionals who want to develop in AI need courses organised around their reality, not the other way around.
Back to HomeHow Cendekia came about
Cendekia began in Kuala Lumpur in 2021, when a small group of practitioners — people who had spent years working in software development, data work, and applied research — noticed a recurring gap. The professionals around them who wanted to build genuine understanding of machine learning were poorly served by what was available: short video libraries that moved too fast, university programmes that assumed a full-time schedule, and bootcamps that emphasised pace over depth.
The response was straightforward. We designed courses the way we would have wanted to take them: with clear prerequisites stated in advance, recorded sessions that respected the fact that some weeks are simply difficult, project-based work that produces something tangible, and access to real human feedback rather than automated rubrics.
The name Cendekia comes from the Malay word for a person of learning — someone who pursues knowledge with care and intention. That framing shapes everything from how our syllabuses are structured to how we talk with prospective learners about whether a course is the right fit for them.
What we are here to do
Depth over surface coverage
We would rather a learner finish a course with a solid grasp of fewer topics than a superficial encounter with many. Each course has a defined scope and stays within it.
Honest fit assessment
We state prerequisites clearly and encourage prospective learners to ask questions before enrolling. A course that is wrong for someone is not a good outcome for either party.
Respectful of your time
Recorded sessions, written feedback, and clearly communicated schedules are built into every course — not added as afterthoughts.
The people behind the courses
Aishah Razali
Course Lead — Machine Learning
Aishah holds an MSc in computational statistics and spent seven years working on modelling systems in financial services before joining Cendekia. She wrote and maintains the Foundations of Machine Learning curriculum.
Farizul Ihsan
Course Lead — Applied Python
Farizul has worked as a software developer and later as a data engineer at several Kuala Lumpur technology companies. His Applied Python course draws directly from the patterns he observed working with production AI systems.
Siti Nabilah
Course Lead — Research Literacy
Siti completed her doctoral work on natural language processing and currently consults for several research-focused organisations. She leads the Reading AI Research Papers short course with a focus on accessible critical reading.
How we maintain course quality
Individual assessment review
Every project submission receives written feedback from the course lead, not an automated response. We limit cohort sizes to keep this sustainable.
Regular curriculum review
AI and machine learning develop quickly. We review course content before each new cohort opens and update material where the field has moved in ways that matter for learners.
Personal data handled carefully
Enrolment information is used only for administration and direct course communication. We do not pass personal data to third parties for promotional purposes. See our Privacy Policy for full detail.
Post-cohort feedback collection
We ask every participant for candid feedback after their course ends. Responses inform both curriculum adjustments and how we describe courses to future learners.
Instructors with practice backgrounds
Each course lead has direct professional experience in their area, not only an academic background. This shapes both the examples used and the focus of feedback on assessments.
PDPA-compliant data practices
Our data handling follows Malaysia's Personal Data Protection Act 2010. Learners can request access to, or deletion of, their personal information at any time.
What it means to study AI with care
Machine learning and AI development are areas where the distance between surface familiarity and working understanding is significant. Someone who has watched video tutorials can speak fluently about gradient descent without being able to apply it to a real problem. At Cendekia, we try to close that gap rather than paper over it.
Our three courses are arranged to support learners at different points. The Foundations of Machine Learning course addresses the conceptual and mathematical base — the kind of grounding that makes later applied work less opaque. The Applied Python course is for learners who already have some programming experience and want to work with AI tooling in a structured way. The research reading short course addresses a different need: the ability to engage with the primary literature on your own terms, rather than relying on summaries.
We work primarily with professionals based in Malaysia and the wider Southeast Asian region, and we are familiar with the particular pressures of studying alongside a full-time role in this context. Our course structures and communication practices reflect that.
If you are weighing whether one of our courses is appropriate for your situation, we are glad to talk through it. An honest conversation before enrolment is more useful to everyone than a mismatch that becomes apparent later.
Ready to take a closer look?
Browse our course details or send us a message. We are based in Kuala Lumpur and glad to answer questions directly.