What People Say After Going Through the Loop
These are the kinds of things learners tell us after finishing. We share them because they reflect what the programmes are actually like — including the parts that were challenging.
← Back to HomeFrom Our Learners
Faizal Harun
Petaling Jaya, Selangor · Foundations
I'd tried to learn Python on my own twice before and given up both times. The difference at Loopmind was that I had to actually submit something each week — there was no way to just watch and feel like I was making progress. Getting written feedback on my code made me understand what I was doing rather than just copying patterns.
June 2025
Lim Yee Shan
George Town, Penang · ML Engineering
The ML Engineering Track was harder than I expected — which is honestly a good thing. Some weeks I submitted code I wasn't confident about and the mentor feedback pinpointed exactly what was shaky and why. I came away with three portfolio pieces I can actually talk about in depth. The one thing I'd have liked is a bit more time on the deployment side, but that's what the Advanced Programme covers.
May 2025
Priya Krishnamurthy
Kuala Lumpur · Advanced Programme
The capstone was the most challenging thing I'd done in an educational setting. It felt like real work — I had to make genuine decisions about architecture, justify them in writing, and then defend them in a review session. Having a dedicated 1-to-1 mentor throughout made that process feel supported rather than overwhelming. Very worthwhile sixteen weeks.
June 2025
Mohd Ruzaini
Johor Bahru · Foundations
I appreciated that the course didn't promise anything about jobs or salaries. It was refreshingly honest — this is what you'll learn, this is what you'll be able to build. The examples used Malaysian data which made the projects feel more relevant than generic tutorials I'd followed before. I finished with a starter project I'm genuinely proud of.
July 2025
Wong Teck Huat
Ipoh, Perak · ML Engineering
I was working full-time through the twelve weeks and was genuinely worried about keeping up. The recorded sessions helped a lot — I could revisit the live content whenever I had time. The cohort channel stayed active between sessions which helped when I got stuck on a problem at an odd hour. The workload is real but manageable if you plan for 6-8 hours a week.
May 2025
Nurul Aina
Shah Alam, Selangor · Advanced
Fine-tuning a pre-trained model was something I'd read about but never done properly before this programme. Getting to do it in a structured way, with someone reviewing my choices and explaining why certain decisions were better-suited to the data — that was the kind of learning that sticks. My capstone is now the first thing I point to when I talk about my AI work.
June 2025
Learning Journeys in Detail
Chandra Harith
Foundations → ML Engineering · 20 weeks total
The Challenge
Chandra worked in data entry and wanted to shift into a more technical role. He had no programming background and was uncertain whether AI development was something he could realistically learn while working full time.
The Approach
He started with the Foundations Programme to build a Python base before moving into the ML Engineering Track the following cohort. The structured pacing and mentor feedback helped him build at a rate that fit his working schedule.
The Outcome
After completing both programmes, Chandra had four portfolio pieces and could discuss feature engineering decisions and model evaluation trade-offs in practical terms. He now uses ML tools as part of his day-to-day role in a data team.
Siti Khadijah
Advanced Programme · 16 weeks
The Challenge
Siti had been using Python and working with standard ML models for about a year independently. She wanted structured exposure to deep learning and deployment practices — but most resources she found were either too shallow or too academic to be useful.
The Approach
The Advanced Programme gave her a structured path through fine-tuning, deployment, and engineering clean code for production. One-to-one mentoring sessions helped her work through blockers specific to her capstone project rather than generic exercises.
The Outcome
Her capstone — a text classification pipeline for Malay-language news content — is the most substantive project in her portfolio. She received specific feedback on her documentation and deployment approach, which she considers the most valuable part of the programme.
Bryan Loh
ML Engineering Track · 12 weeks
The Challenge
Bryan was a web developer who understood programming well but had no machine learning background. He'd tried learning from documentation and MOOCs but kept getting lost once examples moved beyond simple toy datasets.
The Approach
The ML Engineering Track gave him structured exposure to real datasets, evaluation pipelines, and the kind of decision-making that doesn't come up in documentation examples. Code reviews helped him bring his engineering habits into a new domain.
The Outcome
Bryan finished with three portfolio pieces and a much clearer sense of where his engineering skills were strong and where ML introduced different trade-offs. He describes the code review sessions as the highest-value part of the twelve weeks.
Get in Touch
Phone
+60 4 263 9057Location
5A Lebuh Light, George Town, Penang
Hours
Mon–Fri 9am–6pm
Sat 10am–2pm
Loopmind in Numbers
240+
Learners across all cohorts
4.7
Average satisfaction rating
3
Structured AI programmes
3+
Years of cohort delivery
PDPA Compliant
Data handling aligned with Malaysia's Personal Data Protection Act 2010
MDeC Digital Partner
Recognised by Malaysia Digital Economy Corporation's learning ecosystem
Penang Tech Recommended
Recommended by the Penang tech community forum, June 2025
Ready to Start the Loop?
Whether you're at the beginning or picking up where you left off, we'd like to hear where you are and what you're hoping to build. Get in touch and we'll take it from there.
Get in Touch