→ Student voices
Reviews written by students across our three programs. We include a range of experiences, not just the most enthusiastic ones.
← Back to Home340+
students enrolled since 2021
4.6
average program rating (out of 5)
88%
complete the full program
4 yrs
running structured online programs
→ Reviews
Pattaraporn T.
Bangkok · ML Pathway
I came in with some Python knowledge from self-study but had never built anything with real data. The ML Pathway gave me a proper framework for thinking about feature selection — not just what to do, but why certain choices matter. The office hours were useful; I asked about a regression problem in my week 9 notebook and got a clear explanation of where my thinking had gone wrong.
April 2025
Weerasak S.
Chiang Mai · Foundations
The Foundations course was the first programming course I actually finished. I had tried two others before and dropped out around week three. This one felt different — the pace was steady and the weekly notebooks meant I was always producing something, not just watching videos. The feedback on my week 5 submission pointed out a loop structure I had been writing incorrectly for weeks without realising.
April 2025
Nopparat K.
Bangkok · AI Capstone
The checkpoint reviews were the part I underestimated before I started. I assumed I would submit my code and receive general comments. Instead, the mentor read through specific functions and asked questions that made me rethink my pipeline design at week 10. The final panel was a slightly nerve-wracking conversation, but the panel was fair and the feedback was direct and helpful.
March 2025
Siriphon R.
Khon Kaen · ML Pathway
Good program overall. The three portfolio projects are the most useful part — I used two of them to explain my skills during a job interview a few months after finishing. One area I thought could be stronger was the section on evaluation metrics, which felt slightly rushed in the recordings. That said, the written feedback for the related notebook was thorough and covered what the lessons skipped.
March 2025
Apinya C.
Bangkok · Foundations
I enrolled in the Foundations course while working a full-time office job. The self-paced format made it manageable — I typically worked through the week's material on Wednesday evenings and Saturday mornings. The instructor was responsive when I emailed questions, usually replying within a day. By week 6 I was writing functions I would have found completely baffling two months earlier.
April 2025
James W.
Bangkok (expat) · AI Capstone
I work in data analytics and enrolled in the Capstone to push into the engineering side of things. The deployment module was exactly what I needed — I had been hesitant about packaging models for production use. The mentor review at week 15 caught a data leakage issue in my pipeline that could have made the final project unreliable. Worth catching early rather than at presentation.
May 2025
→ Case studies
Challenge
Nattawut worked in digital marketing and wanted to start doing his own data analysis instead of waiting on colleagues. He had tried YouTube tutorials but kept hitting walls when he encountered errors he did not understand.
Solution
Enrolled in the Foundations course and worked through it over nine weeks (took one week of extension). The written feedback on his week 3 notebook explained the error handling pattern he had been misusing, which unblocked several weeks of confusion at once.
Results
By the end of the program, Nattawut was writing Pandas scripts to clean and summarise marketing data himself. He enrolled in the ML Pathway six months later. "The feedback on that third notebook changed how I read error messages entirely."
Challenge
Kanokwan was a backend developer with five years of experience who wanted to move into machine learning work. She could write Python well but had no framework for thinking about model evaluation or feature selection.
Solution
Enrolled in the ML Pathway. Attended every office hours session and used them to explore decisions in her portfolio projects beyond what the notebooks covered. Her third project used a real dataset from her current employer.
Results
Three months after completing the program, Kanokwan moved to an ML engineering role at her company. The portfolio she built during the program formed the basis of her internal pitch. "Having three concrete projects to show made the conversation much easier."
Challenge
Thanakorn worked in a data team and was comfortable with model training but had never built a production-ready pipeline or deployed a model outside of a notebook environment. His team was being asked to ship more work into production.
Solution
Enrolled in the Capstone. The checkpoint reviews at weeks 5 and 10 restructured his approach to pipeline design before he had invested too much work in a flawed architecture. His capstone project was a small deployment of a classification model his team had been discussing.
Results
The capstone project became the basis of a real deployment at his company two months after the program ended. The panel presentation helped him practise explaining the technical choices to a non-technical audience. "The checkpoint at week 10 probably saved me from rebuilding everything at week 18."
Address
357 Lat Phrao Soi 71,
Wang Thonglang, Bangkok
Office Hours
Mon–Fri, 09:00–18:00 ICT
→ Your turn
Tell us where you are with programming and what you are hoping to work toward. We will suggest the right starting point.
Get in Touch