# cell 01 — from learners
What People Who Have Done the Work Say
Reviews from learners who completed programmes at Tunas Nusantara. Unedited and honest — including the ones that mention what was difficult.
← Back to Home# cell 02 — reviews
From the last few intakes
Ahmad Hisyam
Petaling Jaya · Applied ML
I had done self-study before but could never tell if my model evaluation was right or just lucky. The feedback on project two — the one on feature engineering — was the most useful thing I got in the whole sixteen weeks. The instructor pointed out specifically why two of my features were leaking information from the test set. No online course had ever caught that.
July 2025
Nurul Zahra
Kuala Lumpur · Python Intro
I came from accounting with zero coding background. I had tried two free courses before and stopped after week one both times because the examples made no sense to me. Here they started with something I could picture — a small dataset of figures — and built the code from that. Four weeks was genuinely enough time to feel like I understood what I was writing. The office hours helped a lot in week three.
June 2025
Reza Fahmi
Subang Jaya · Deep Learning
I used the free repeat option after my first run through — the material on regularisation did not click the first time for me and I felt I was cargo-culting the techniques rather than understanding them. The second pass was much clearer. I would prefer if that option were mentioned more prominently at enrolment rather than something I had to ask about, but I am glad it exists.
July 2025
Suria Wahab
Shah Alam · Applied ML
The evening scheduling genuinely made this possible for me. I work until six most days and the live sessions starting at eight meant I could eat dinner first and still make it. I missed two sessions in week seven because of a deadline at work but the recordings were up by the time I got home, so I caught up over the weekend without losing the thread.
June 2025
Kavita Loganathan
Cyberjaya · Python Intro
The two-week withdrawal option is mentioned in the course description and it changed how I approached the first two weeks. I was not trying to decide if I could make it to the end — I was just trying to see if the content was something I could work with. By week two I knew I wanted to keep going. That framing made it easier to commit.
July 2025
Mohd Bazli
Klang · Deep Learning
The mentor report on my training runs after week six was uncomfortably specific — it pointed out that my validation loss plateau was happening too early and explained why that was likely a learning rate issue rather than a data problem. I had assumed the opposite. The specificity of the feedback is what makes this worth paying for.
June 2025
# cell 03 — case studies
Three learner journeys in more detail
case study 01 · Python → Deep Learning pathway
From finance analyst to neural network training
Starting point
Worked as a financial analyst in KL for four years. Comfortable with Excel and basic SQL but had never written Python. Started the Python for AI Work module after seeing colleagues use data pipelines and wanting to understand what they were doing.
Path through the programmes
Completed Python in four weeks, took a two-month break to practice independently, then enrolled in Deep Learning Foundations. Used the free repeat option for the regularisation section and found the second pass much clearer than the first.
Outcome after 14 months
Now writes Python scripts to automate parts of the financial reporting process at work and has a side project using a small neural network for time series forecasting on public market data.
case study 02 · Applied ML from a software background
Understanding model evaluation properly for the first time
Starting point
Software developer in Cyberjaya, three years experience mainly in backend web work. Had built a classifier following online tutorials but did not understand whether the evaluation metrics being reported were reliable.
Focus during the programme
Entered directly into Applied Machine Learning after passing the entry check. Spent most of the sixteen weeks focused on the evaluation and deployment sections. The feedback on project three identified a target leakage issue that had made previous results look better than they were.
Outcome
Rebuilt the classifier from project two of the programme properly and now uses cross-validation with a held-out test set as standard practice. The deployment project in week fifteen is now a small internal tool used by the team.
case study 03 · Career change from teaching
Starting from scratch with Python at 34
Starting point
Secondary school maths teacher in Seremban, no programming experience. Decided to investigate AI after seeing it discussed in educational contexts and wanting to form a first-hand opinion rather than rely on summaries.
Experience in the programme
Completed Python for AI Work over four weeks. Nearly withdrew in week two when the dataframe exercises felt overwhelming, but asked in the help channel and got a response the same evening that reframed the approach. Finished week four and described it as the first time code had felt like something she was in control of rather than guessing.
Outcome
Not pursuing a career change at this point, but uses pandas to manage class data and assessment results and plans to follow the Applied ML programme in 2026 once personal commitments allow the ten hours per week.
# cell 04 — reach us
Contact Details
- +60 3 2148 7362
- [email protected]
- 8 Jalan Bukit Bintang, 55100 Kuala Lumpur, Malaysia
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Office Hours
Mon–Fri: 10:00 – 18:00 MYT
Sat: 10:00 – 13:00 MYT
# cell 05 — by the numbers
Where the school stands
# cell 06 — join the next cohort
See the first week's material before you decide
Every programme makes Week 1 available to read at no cost. Or contact us with a description of your background and we will suggest the right starting point.
Get in Touch