What People Say
About the Work
These are real accounts from learners who worked through the Neuronest tracks. They reflect a range of backgrounds, goals, and experiences — including where things were difficult.
← Back to Home// since 2022
// across all tracks
// above industry average
// primary growth channel
From the Learners
A mix of accounts from across all three tracks — foundations through to career-readiness.
Pichaya Thongkham
Bangkok — AI Foundations Track
I came in with basically no coding background — just some spreadsheet experience. The Foundations Track was genuinely more work than I expected, but the structure helped. My mentor, Nattaya, was patient with questions that were probably quite basic, and never made me feel like I was slowing things down. By week 6 I was writing actual data scripts. Took me the full 12 weeks to get comfortable. Realistic track for a real beginner.
// June 2025
Wichai Rattanaporn
Chiang Mai — Applied ML Studio
I'm a backend developer and the ML Studio was the right fit — not too easy, not so far ahead that I was lost. The code reviews were useful; I had some bad habits in how I was structuring data pipelines and the mentor caught them clearly in week 3. Losing one star because the deployment section felt a bit rushed at the end, though it was listed as an intro and I knew that going in. Would recommend to other developers looking to move into applied ML.
// May 2025
Siriporn Kaewmanee
Bangkok — AI Foundations Track
What made the difference for me was the weekly session format. I'd tried two other online courses before this and dropped off around week 4. Having a fixed time each week where someone was actually checking in on my work made me show up. The content wasn't complicated, but it was well sequenced. I felt like things were building on each other rather than just being random topics. Solid starting point.
// June 2025
Attapol Phanich
Bangkok — Career-Readiness Track
I did the Career-Readiness Track after working through the ML Studio. Kasem was my mentor and he was straightforward about where my code was weak, which I appreciated. The system design modules pushed me to think differently about AI work than I had before — less notebook-focused, more about what happens when something has to run in production. The interview prep was also genuinely useful; not generic advice but practice on the kinds of questions I'd actually face.
// May 2025
Narumon Boonnak
Khon Kaen — Applied ML Studio
I'm based in Khon Kaen and was worried about the timezone — it wasn't an issue at all. Sessions happen within Bangkok hours which works fine from anywhere in Thailand. The ML Studio was harder than I expected in weeks 5–7 when we got into evaluation methods; I had to revisit some concepts a few times. But my mentor was accessible and responded to messages usually the same day. Portfolio looks solid now and I understand what I built.
// June 2025
Chalit Srirung
Bangkok — Career-Readiness Track
The peer review element in the Career-Readiness Track is something I didn't expect to value as much as I did. Reading other people's code and having them read mine was a different kind of feedback than what the mentor gives. Before this track I had never worked with anyone else on AI code — at my job I was the only person doing it. The cohort dynamic changed how I think about code quality.
// May 2025
Learner Journeys in Detail
Three accounts of what the experience involved — challenge, process, and where learners ended up.
Kanokwan Suwanprasert
Marketing analyst, Bangkok — AI Foundations → Applied ML Studio
The Challenge
Kanokwan had been working in marketing analytics for four years — comfortable with Excel and basic statistics, but no Python experience. She wanted to understand the ML models her data team was building, but didn't know where to start. She'd tried a free Python course and got through lesson 3 before dropping it.
The Process
Started with the AI Foundations Track — 14 weeks of steady work at about 8 hours per week. The weekly check-ins kept her from drifting. After completing the Foundations Track, she moved into the Applied ML Studio, where she built three projects applying ML to real marketing datasets.
Where She Is Now
After 28 weeks across both tracks, Kanokwan can write and evaluate ML models in Python, understands the data pipelines her team uses, and has a portfolio of three projects. She continues working in her marketing role but collaborates more directly with the engineering team.
"The sequencing across both tracks was clear. I wasn't jumping around — each week built on the last. That's what was missing in everything I'd tried before."
Thanawat Niyomkul
Software developer, Bangkok — Applied ML Studio
The Challenge
Thanawat had strong Python skills and four years of backend development experience. He'd read the documentation for several ML libraries but couldn't bridge the gap between toy examples and work that felt production-appropriate. His self-taught models evaluated well on training data and poorly on anything else.
The Process
Joined the Applied ML Studio. Found that the structured evaluation methodology in weeks 5–8 directly addressed his overfitting problem. His mentor reviewed his model evaluation code in depth and identified where his validation approach was leaking data. Rebuilt two projects using corrected methodology.
Where He Is Now
Completed the Applied ML Studio with a portfolio of four applied projects. He's now working through the Career-Readiness Track. The evaluation skills transferred directly to his day job where he now reviews the data team's model testing approaches.
"Having someone look at my actual code rather than tell me the general theory was the specific thing I needed. I knew the theory. I didn't know where I was applying it wrong."
Reach the Team
Questions about any of the tracks? We respond during working hours.
// address
222 Ratchadaphisek Rd
Huai Khwang, Bangkok 10310
// office hours (ICT)
Mon–Fri: 09:00–18:00
Sat: 10:00–14:00
Ready to Begin?
Send us a message and we'll have an honest conversation about which track fits your current background and what to expect from the work.