// About the school
Building practical AI knowledge
one programme at a time
Akar Tensor was set up in Kuala Lumpur to fill a specific gap: structured, evening-format programmes for people who already work and want to extend their technical capability in machine learning and AI development.
← Back to HomeHow Akar Tensor came about
The name Akar comes from the Malay word for root. We chose it because practical AI work starts at the roots — probability, linear algebra, data handling — before any of the more visible tooling makes sense. Tensor is what holds the numbers together. The name reflects what the programmes try to do: give practitioners a solid base they can build from.
Akar Tensor started in 2021 with one programme: a ten-week evening course in the foundations of machine learning. At the time, most available options were either full-time bootcamps that required leaving employment, or self-paced online courses with no cohort and no feedback loop. Neither worked well for developers and analysts who had a day job and wanted to grow into ML work over a few months.
The evening format — two sessions per week, Monday and Wednesday, 7:30 to 9:00 PM — was the starting point. Small cohorts followed. Instructor review of assignments, rather than auto-grading, came next. The Language Model Engineering programme was added in mid-2023 when the demand for practical LLM application skills became clear. The Data Preparation short course was developed in response to a recurring observation: many candidates for the ML programme needed more time with the data pipeline work that precedes any modelling.
We are based at Level 12, Menara Sentral in KL Sentral, which is accessible by KTM Komuter, LRT, and MRT from most parts of the city. Sessions run in person. Recordings are available to enrolled participants for the duration of the programme.
The team
The people who design, deliver, and review work in each programme.
Zainal Abidin
Programme Director · ML Foundations
Zainal designed the ten-week ML Foundations curriculum and leads most live sessions. His background is in statistical computing and data engineering, with eight years at firms in KL and Singapore before moving into education full-time.
Siti Rahayu
Lead Instructor · LM Engineering
Siti built and leads the Applied Language Model Engineering programme. She spent four years building internal NLP tooling for a fintech company in KL before joining Akar Tensor. She reviews the running projects each cohort produces.
Fadzilah Hassan
Instructor · Data Preparation
Fadzilah developed the Data Preparation short course and runs the four live workshops. Her professional focus is on data pipeline reliability and she holds an MSc in Computer Science from Universiti Malaya.
Programme standards
The practices we apply consistently across all programmes.
Stated Prerequisites
Every programme publishes its prerequisites before enrolment opens. We do not accept participants who have not met the stated technical baseline, to avoid situations where one student's struggle slows the cohort.
Instructor-Reviewed Work
Assignments are reviewed by a human instructor, not a grading script. Written comments are returned within five working days of each submission deadline.
Cohort Caps
The ten-week ML Foundations programme limits enrolment to 24 participants. This keeps live sessions manageable and means questions receive direct answers rather than being deferred.
Data Privacy
Participant data is held in line with the Malaysian Personal Data Protection Act 2010. We do not share enrolment information with third parties. Session recordings are accessible only to enrolled participants.
Current Curriculum
Syllabi are reviewed before each new cohort. If a library or service has changed significantly, the relevant session is updated. We note changes in the programme changelog so returning participants know what is different.
Transparent Pricing
Fees are published on the solutions page. For the LM Engineering programme, the approximate third-party API cost (RM 120 over the term) is stated separately so the full cost of participation is clear before any payment is made.
What we think good technical education looks like
A lot of technical education is sold on outcomes it cannot reliably produce. We do not make claims about what participants will be able to do after completing a programme, because what someone takes from a course depends heavily on what they bring to it, how much they practise, and what problems they happen to encounter next. What we can control is the quality of the material, the accuracy of what we say the programme covers, and the consistency of instructor feedback.
We write the same information three times for each programme: in the full syllabus, in the prerequisite checklist, and in the tooling list. Someone reading all three before enrolling should have no surprises about what they are signing up for. If the material is not the right fit, it is better for both parties to find that out before payment is made.
Evening education in Kuala Lumpur benefits from the city's infrastructure. The KL Sentral location is reachable from most of the Klang Valley within 30 to 45 minutes using public transport, which reduces the friction of attending twice-weekly sessions after a full working day. The MRT Putrajaya Line, KTM Komuter, and ERL all stop at or within walking distance of Menara Sentral.
The programmes are in English. The technical vocabulary in machine learning and language model development is predominantly English regardless of the language of instruction, so we work in the language the field uses.
See which programme fits where you are now
Review the prerequisite lists on the Solutions page or send us a brief note about your background. We will tell you honestly whether you are ready for a given programme or whether the short course would be a better starting point.
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