What people say about
learning at Tunas AI.
These are honest accounts from people who completed courses here. We include the mixed experiences, not just the glowing ones.
Back to HomeLearner accounts.
I had tried learning Python twice before on my own and kept giving up around week three. The difference here was the weekly clinic — having a space to ask questions before they piled up made the material stick in a way self-study hadn't. The pace was comfortable without feeling slow.
The ML track was genuinely challenging. I work full time and at points the reading load was heavier than I had expected. That said, the two projects were worth the effort — I now have work samples I can actually discuss in detail. The feedback on both was specific and useful, not just general comments.
I enrolled to see whether I could manage technical content after years outside a STEM field. The answer turned out to be yes. The material was explained in a way that didn't assume background knowledge I didn't have. Finishing the final project felt like a meaningful thing, not just a box to tick.
Track 03 is the most technically dense thing I have done in a while. The instructor's written comments on the capstone were the most useful feedback I have received on code in years — direct, with reasons, not just corrections. The alumni channel has been active and useful even a few months after graduating.
The cohort format helped. Working through the same material with a small group meant I could check my understanding against others, and the peer channel was actually active during the course. I would have liked slightly more coverage of cross-validation, but the feedback on Project 2 addressed some of that gap.
I joined from Penang and everything ran smoothly online. The session recordings were a real help — I missed two live sessions due to work and was able to catch up without falling behind. By the end I had a working data analysis script and a clearer idea of what I wanted to explore in Track 02.
Learner journeys, in detail.
From HR Analyst to ML Practitioner
Wanida worked as an HR analyst with no coding background. She needed to understand and eventually contribute to data modelling work within her organisation, but had no clear starting point.
She completed Track 01 over six weeks, then enrolled in Track 02 four months later. The two-track progression gave her time to apply Python basics at work before moving into machine learning methods.
By the end of Track 02, Wanida had built a classification model for employee churn prediction as her second project. The work was shared internally and led to a change in her job scope to include data analysis responsibilities.
"I didn't expect to have something portfolio-worthy after ten weeks. The feedback pushed the quality of the project higher than I would have managed alone."
Building a Deployment-Ready Image Classifier
Hazwan was a backend developer who understood machine learning conceptually but had never trained a model or deployed one. He could read ML papers but couldn't yet implement the ideas in code.
He joined Track 03 directly, having covered Python and basic data work on his own. Over fourteen weeks he worked through convolutional architectures and built a capstone that used a trained CNN for a product categorisation task.
The capstone included a simple inference API. Hazwan deployed it to a cloud instance and was able to demonstrate it as a working service. He has since used the same pattern at work on a small internal tool.
"The deployment module was what made it real. A trained model sitting on a laptop is one thing. Having it respond to API calls is another."
A First Project in a New Field
Preethi was a secondary school science teacher exploring a career shift toward educational technology and data. She had done some research on Python online but felt the material moved too quickly for someone without a technical degree.
Track 01 over six weeks. She attended four of the six clinic sessions live and watched the other two recordings. Her final project involved analysing student performance data from a public dataset relevant to education in Malaysia.
At the end of the course, Preethi had a working data analysis script and the confidence to continue learning independently. She said the course gave her a sense of what she still needed to learn, which she described as "the most useful outcome."
"I left knowing what I didn't know yet, which is more useful than leaving with the impression you've covered everything."
Contact information.
Sat: 10:00 am – 2:00 pm (MYT)
Credentials.
- Malaysia Digital Economy Community Member (since May 2024)
- Best New Tech Training Provider — KL EdTech Circle 2024
- Associate Partner — MDEC AI Skills Programme (since January 2025)
- PDPA 2010 compliant data handling across all operations
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