Student Experiences
What Students Say After Finishing
These are accounts from people who have completed a Cerdik Code programme and agreed to share their experience. The details are theirs.
Back to Home140+
Students completed
4.8
Average rating out of 5
92%
Would recommend to others
3
Structured tracks offered
What Students Say
Reviews From Recent Cohorts
Dates shown are from the month each student submitted their review.
Faridah Hamid
Administrative Officer · Kuala Lumpur
"I had no background in coding at all when I joined the AI Literacy track. The mentor was very patient and the pace was manageable alongside my work schedule. By week four I had something running in Python and actually understood what it was doing — which surprised me."
AI Literacy for Beginners · May 2025
Khoo Wei Liang
Data Analyst · Penang
"The ML Practitioner track covered the workflow I had been trying to piece together from tutorials for months. Having someone review my project and write back specifically about my choices — not generic tips — was the part that actually moved things along. I would have liked slightly more time on model deployment but that is a minor thing."
ML Practitioner Track · April 2025
Rajan Arumugam
Software Engineer · Petaling Jaya
"I joined the Deep Learning Studio after completing the practitioner track. The capstone format was what I was looking for — you propose something you actually want to build and the mentor keeps you honest about scope and approach. The alumni space has been quietly useful even after the programme ended."
Advanced Deep Learning Studio · May 2025
Siti Norliza
HR Executive · Johor Bahru
"Cerdik Code was the third AI course I tried. The first two I dropped because the pace was too aggressive and I felt lost by week two. Here the beginner track was genuinely beginner level — it did not assume hidden knowledge. The live sessions helped a lot."
AI Literacy for Beginners · May 2025
Lim Ming Chuan
Operations Manager · Ipoh
"The ML track gave me enough to have informed conversations with the technical team at work, which was really my goal. I did not need to become a data scientist — I needed to understand what they were doing and when to push back on their assumptions. This did that."
ML Practitioner Track · April 2025
Nazrin Bakar
Python Developer · Kuching
"I completed the Deep Learning Studio as a developer moving from web to ML work. The ethics discussions were more substantive than I expected — not just a warning section but woven into the modelling decisions themselves. The capstone review process was thorough. Mentor feedback was direct and useful."
Advanced Deep Learning Studio · May 2025
Case Studies
Three Student Journeys
Faridah Hamid
Administrative Officer → AI-Aware Staff Member · 5 weeks
Challenge
Faridah wanted to understand the AI tools her organisation was starting to evaluate, but had no technical background. She felt uncertain talking with vendors or interpreting the claims being made in presentations.
Path Taken
She joined the AI Literacy for Beginners track and worked through the five-week programme alongside her full-time role, spending about six hours per week on coursework and attending the live weekly session.
Outcome
After completing the track, Faridah described being able to evaluate vendor claims more clearly and ask better questions in meetings. She also contributed a short briefing note to her team on what AI can realistically handle in their context.
"I was not trying to become a developer. I just needed to understand what was being proposed. This gave me that."
Khoo Wei Liang
Data Analyst → ML-Capable Practitioner · 10 weeks
Challenge
Wei Liang had been using Excel and basic SQL for analysis and wanted to move into predictive modelling. He had tried to learn from free online courses but found the gap between tutorial code and actual project work too large to bridge on his own.
Path Taken
He joined the ML Practitioner Track with some basic Python knowledge from prior study. Over ten weeks he worked through two projects using datasets relevant to logistics and retail — sectors he was already working in.
Outcome
By the end of the track, Wei Liang had completed two model pipelines with mentor review on both. He moved into a junior data science role within two months of completing the programme. He reported that the mentor feedback on his first project was the turning point.
"Having someone actually read my project and tell me what was wrong with my evaluation approach — that was worth the fee by itself."
Rajan Arumugam
Software Engineer → Deep Learning Practitioner · 14 weeks
Challenge
Rajan had completed the ML Practitioner Track and wanted to go deeper into neural networks, specifically for a computer vision project he had been considering at work. He needed structured guidance and a space to test an approach before committing to it at his job.
Path Taken
He enrolled in the Advanced Deep Learning Studio and proposed a capstone focused on defect detection in manufacturing images — a direct reflection of his professional context. The mentor worked with him to scope the project realistically and reviewed progress at each stage.
Outcome
Rajan's capstone produced a working prototype with 87% detection accuracy on a test dataset. He presented the work to his team, which led to a small internal pilot. He stayed active in the alumni circle and contributed to a discussion on data augmentation strategies two months after completing.
"The capstone format forced me to commit to a direction early, which was uncomfortable but ultimately useful. I had something real at the end."
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