What people have made, and how they found the process
These are accounts from people who have gone through the programmes — what they found useful, what was difficult, and where they ended up.
Back to Home250+
Learners enrolled
4.7
Average satisfaction
3
Years operating
87%
Completion rate
From the learners themselves
Siti Liyana Mohd Razi
Petaling Jaya · Getting Started
I had no programming background and was anxious about that. The first two weeks were slower than I expected, which was actually fine — I needed that pace. By week five I was cleaning and analysing a dataset I had downloaded myself, and that felt significant.
May 2025
Rizwan Abdul Karim
Kuala Lumpur · Machine Learning
The retail project in week seven was where things clicked for me. I had done plenty of tutorials before, but building something from a real-ish dataset — with a mentor who actually read my code and pointed out where my thinking was off — was a different experience. I would have liked slightly more time on the evaluation section, but overall solid.
April 2025
Tan Wei Xuan
Penang · Production AI
I work as a backend developer and had trained models before, but deploying and monitoring them was always someone else's problem. This track fixed that. The containerisation module was particularly useful, and the peer review sessions on the production project were genuinely helpful — everyone was there to improve the work, not perform.
May 2025
Nurul Farahain Zainudin
Shah Alam · Getting Started
I was working full-time and studied in the evenings. The lesson structure made that manageable — nothing was so long that you could not finish it in a sitting. The weekly call helped me stay on track because there was always something coming up that was worth preparing for.
April 2025
Karthigeyan Ponnusamy
Johor Bahru · Machine Learning
The logistics dataset in the second project was closer to what I actually deal with at work — which made the whole thing feel less academic. Written feedback on my first project submission was detailed enough that I spent two days reworking the feature engineering before resubmitting. Worth doing.
May 2025
Lim Huey Shan
Kuala Lumpur · Production AI
I had completed the machine learning track the previous year and came back for the production one. The progression felt natural — concepts from before came up again in more complex forms. Having alumni access after finishing is something I have already used twice to look back at the monitoring materials.
May 2025
A closer look at a few journeys
From admin work to data analysis in six months
Getting Started → Machine Learning Track · 6 months total
Challenge
An administrative officer at a Klang Valley distribution company wanted to understand the data her team was collecting but had no programming background. She was unsure whether to enrol because she had no technical qualifications.
Approach
She started with Getting Started with Data and Python and completed it over seven weeks rather than six. After a month's break she enrolled in the Machine Learning track. The projects she chose focused on logistics data, directly relevant to her workplace.
Outcome
By the end of the machine learning programme, she had two completed projects in a GitHub repository and had begun running exploratory analyses in her day job. Her team lead asked her to present the results at a monthly operations meeting.
"The enrolment team was honest with me about what the programme could and could not do. That helped me set the right expectations before I started."
Challenge
A backend developer with five years of experience had experimented with machine learning in notebooks but had never shipped a model to a production environment. The gap between notebook and deployment felt opaque.
Approach
He enrolled in the production track and worked through containerisation, API serving, and monitoring in sequence. The peer review format suited him — he found the code reviews more useful than the lesson material in the later weeks.
Outcome
His programme project — a deployed product recommendation service — is now running in a personal cloud account and forms the centrepiece of his portfolio. He reported that the monitoring section changed how he thought about software reliability more broadly.
"I came in thinking the hard part was the model. The production track taught me the hard part is everything that happens after you have one."
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Curriculum informed by practitioners from retail, logistics, and financial services sectors
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