What people say after going through the courses
Reviews from learners who have completed one or more Kasturi Tech tracks. Different backgrounds, different outcomes — written in their own words.
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Years running cohorts
180+
Learners trained
4.8
Average end-of-cohort score
MDEC
Digital Skills Partner
From the learners themselves
Ahmad Hamizan
Petaling Jaya · AI Starter Course
"I came in with no coding background at all — not even a basic script. The first two sessions were the slowest I had done anything technical and I mean that as a compliment. By week four I could read a dataset properly and understand what the functions were doing. The project at the end was small but it was mine, and I knew exactly how it worked. That was not something I expected going in."
April 2025
Siti Norzahra
Kuala Lumpur · ML Track
"The Machine Learning Track was harder than I expected, which in retrospect is exactly right. I had done online tutorials before and thought I understood training, but when I had to explain my evaluation choices on the first project, it became clear I had been copying patterns. The feedback I received was detailed enough to be uncomfortable, and then useful. Second project went much better."
April 2025
Rajan Krishnan
Shah Alam · Deep Learning Track
"The capstone review process was the most useful part. Instead of a single submission and a score, we went through the project in stages — architecture choice first, then training setup, then evaluation, then deployment plan. Each stage got specific notes. By the time I submitted the final version I had a model I could actually explain to someone else."
May 2025
Lim Wei Jian
Subang Jaya · AI Starter Course
"Evenings worked for me. I have a full-time job and the sessions ran at 8pm which meant I could attend without taking leave. The recordings were also useful — I went back to two of them the following weekend when I was working on exercises. The clinic felt small enough that asking questions was not embarrassing."
May 2025
Nur Fatin Aisyah
Cyberjaya · ML Track
"What I valued was that nobody pretended the field is simpler than it is. When there were genuinely competing approaches, the session would lay out both and explain the tradeoffs instead of just picking one for us. The peer channel was active and the questions people asked were often ones I had not thought of yet."
May 2025
Zainal Abidin
Ampang · Deep Learning Track
"I had followed a few other online AI courses before this. The difference here is that the sessions are live and the instructors are present in the way that makes a difference — they know who is asking, they know what you are working on. That changes the quality of the answer. I am still in the alumni space and still finding it worth reading."
April 2025
Longer journeys, specific results
From spreadsheet analyst to ML practitioner
Learner: Hafizuddin Ismail · AI Starter → ML Track
Challenge
Hafizuddin had worked with Excel and basic statistics for several years but had no programming experience. He wanted to start building predictive models for his team's supply chain data and had tried learning Python through free online courses but kept stopping when the exercises became unclear.
Approach
He enrolled in the AI Starter Course first and spent six weeks on Python foundations and basic data handling. The clinic sessions helped him resolve specific blockers each week. After completing the Starter Course he moved into the Machine Learning Track, applying his supply chain data as the basis for both track projects.
Results
By the end of the Machine Learning Track, Hafizuddin had built a working demand forecasting model using his organisation's own data, with an evaluation structure he could explain to his manager. His second project was used as a reference example in a subsequent cohort, with his permission.
"I stopped worrying about whether I belonged in AI and started just working on the problem."
Building deployment confidence after self-study
Learner: Priya Damodaran · Deep Learning Reasoning Room
Challenge
Priya had two years of self-taught deep learning experience and could train models from tutorials, but had never taken one through to deployment. She had attempted it twice and both times the project stalled at the serving stage. She was not confident about whether her training decisions were sound or just lucky.
Approach
She joined the Deep Learning Reasoning Room at the advanced entry level. The staged capstone process meant her architecture choices were reviewed before she committed to training, which surfaced two decisions she had not questioned before. Deployment was covered across three sessions with enough depth to resolve her earlier blockers.
Results
Priya completed a served image classification model as her capstone and documented the process clearly enough to hand it over to a colleague. She noted that the staged feedback changed her understanding of her own prior work — she could now explain where her earlier models had been fragile.
"The staged reviews meant I caught a design issue at week five that would have wasted weeks of training time."
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