What learners
actually say.
Reviews from people who enrolled, did the work and came out the other side with something to show for it.
340+
Learners enrolled
4.7
Average satisfaction score
82%
Cohort completion rate
3 yrs
Running AI cohorts in KL
From the cohorts
Nur Zahira
Data Analyst, Petaling Jaya
"I had been working with data for four years and felt like I understood what was happening in models without really knowing why. The Foundations track changed that. The tutor feedback on my regression exercises was detailed enough to be genuinely useful — I rewrote two of them and understood the difference. Worth the eight weeks."
June 2025 — FoundationsKelvin Wong
Software Engineer, Kuala Lumpur
"Deep Learning was harder than I expected in terms of hours — I probably underestimated the second live session each week. But the GPU credits were there when I needed them and the four assessed projects actually built on each other, which made the final one feel achievable. The tutor found a flaw in my evaluation pipeline that I would have shipped to production."
June 2025 — Applied Deep LearningPriya Ramasamy
ML Engineer, Shah Alam
"I did all three tracks. Foundations gave me vocabulary I had been faking for two years. Deep Learning gave me the architecture knowledge to read papers. The Capstone gave me a project I could actually explain at an interview — from the problem statement through the training decisions to the deployment. The weekly mentoring sessions with Siti were the most useful part."
July 2025 — CapstoneFaizal Sulaiman
Operations Manager, Johor Bahru
"I came in with Python basics but genuinely no statistics background. The first two weeks of Foundations were harder than I expected, but the forum was active and questions got answered the same day. By week four things started clicking. I took longer to finish the exercises than some cohort members but the tutor feedback was the same quality regardless."
May 2025 — FoundationsAisha Chin
Product Manager, Cyberjaya
"My goal was to understand what my engineering team was actually doing so I could ask useful questions. The Foundations track did that. I do not think I would sign up for Deep Learning — I am not writing the code day to day — but knowing the vocabulary and the general shape of the training process changed how productive my conversations with engineers are."
June 2025 — FoundationsRajan Muthusamy
Backend Developer, Georgetown
"The prerequisite lattice was something I did not see anywhere else. Before I enrolled I was not sure if I had enough background for Deep Learning — the lattice made it possible to figure that out myself instead of hoping for the best. I ended up doing Foundations first. That was the right call."
July 2025 — Foundations → Deep LearningLearner journeys in detail
Kelvin Wong — Software Engineer, KL
Applied Deep Learning Track · 16 weeks
Starting point
Kelvin had five years of backend development experience and had taught himself basic neural networks from online material, but could not get models to generalise reliably and had no clear way to debug training problems.
Through the track
The track's focus on experiment tracking and systematic evaluation gave Kelvin a framework for diagnosing model behaviour. His third assessed project — a sequence model for time-series data — was reviewed in detail and he rewrote the evaluation section twice based on tutor feedback.
Where he ended up
Kelvin moved into an ML engineering role at his company within two months of finishing the track. He cites the evaluation pipeline understanding as the most direct contribution — it was the main thing he was asked about in his internal interview.
Priya Ramasamy — All Three Tracks, Shah Alam
Foundations → Deep Learning → Capstone · 48 weeks total
Starting point
Priya was a mid-career engineer working in a different technical domain who wanted to move into machine learning. She had some Python experience but no ML background and needed to build from the ground up while continuing to work full time.
Through the tracks
She progressed through all three tracks over about a year. The Capstone project — a document classification system — was scoped in the first mentoring session with Siti, built over six months with weekly check-ins, and deployed to a cloud environment with a written technical report.
Where she ended up
Priya accepted an ML engineer position with a KL-based company, citing the Capstone project as the primary portfolio piece that moved her application forward. The interview practice workshops in the Capstone programme helped her frame her non-traditional background as a practical advantage.
Reach the team
Phone
+60 3-2091 6473Address
30 Jalan Raja Chulan, 50200 KLOffice Hours
Mon–Fri 10–19 MYTSat 10–15 MYT
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