What Learners Say About Their Experience
Unedited accounts from people who've completed Weightspace courses. Including what took more effort than they expected.
Back to Home200+
learners enrolled
4.7/5
average rating
3
years of courses
88%
course completion rate
Feedback from Our Students
Nattapong Phromma
Bangkok · Coding Foundations
June 2025
I had zero coding experience before this. The course is slower than I expected — in a good way. Each lesson builds on the previous one without jumping ahead. The exercises are where you actually figure out what you understood and what you didn't. It took me about seven weeks working 6 hours a week.
Siriporn Kaewsai
Chiang Mai · ML Projects
June 2025
The mentor feedback on my code was the thing that made this worth the price. Not just "looks good" — they would point out specific issues with my approach and explain why another method would be more appropriate. The real datasets were messier than I was used to, which was frustrating at first but is actually closer to what real work looks like.
Arthit Tanasombat
Khon Kaen · Cohort Track
May 2025
The Mentorship Track is demanding — they tell you that upfront and it's true. I was working 13–14 hours a week on this while also having a day job. But having a mentor who actually reads your work and talks you through problems is a different kind of learning. The cohort group was small, which meant you actually got to know the other learners a bit.
Pimchanok Wongso
Phuket · Coding Foundations
May 2025
I'd tried a couple of free Python tutorials before this and kept getting lost when things got more complex. The Weightspace course explains each new idea in context, so you understand why you're learning it. My only note is that I wished there were a few more practice problems in the later lessons. But as a starting point for AI development, it's solid.
Rattana Limwong
Bangkok · ML Projects
June 2025
What I appreciated was that nobody promised this would get me a job. The focus is on whether you actually understand the material. The portfolio project at the end is something I built myself, not a template I modified. That distinction matters when you're trying to actually learn versus just getting something to show.
Chaiyot Bunyarat
Udon Thani · Cohort Track
May 2025
I had the initial conversation with the team before joining the Mentorship Track and found it useful — they asked about my background and were straightforward about whether the programme was likely to work for where I was. The weekly structure kept me accountable in a way that self-paced courses don't. Not easy, but it worked for me.
Three Learning Stories in Detail
// the challenge
Starting from Zero
Narumon had a background in business administration and wanted to understand the data work her technical colleagues were doing. She had no coding experience and wasn't sure where to begin.
// the path
Coding Foundations, at Her Own Pace
She enrolled in Coding Foundations and worked through it over about nine weeks, spending her evenings on the exercises. She contacted the support team twice when specific exercises weren't clear and got helpful responses the next day.
// after finishing
A Working Python Foundation
By the end she could write Python scripts to process data files, understood how to read unfamiliar library documentation, and had a clearer idea of what her technical colleagues were working with. She then enrolled in the ML Projects course.
"I didn't know what I was doing at the start. By the end I at least knew what I didn't know yet, and that felt like real progress." — Narumon S.
// the challenge
Coding Without Direction
Thanachai had been learning Python from YouTube tutorials for about a year but felt like his knowledge was scattered. He could write basic scripts but didn't know how to approach a real data problem from start to finish.
// the path
Hands-On ML Projects Course
The structured progression of the ML course gave him a framework he'd been missing. The code review sessions were the part he found most valuable — the feedback helped him understand not just what was wrong but how to think about the problem differently.
// after finishing
A Portfolio Project and a Clearer View
He completed the portfolio project — a classification model on a real housing dataset — and found the process of building it from scratch much more instructive than following a tutorial. His understanding of model evaluation in particular deepened significantly.
"The feedback on my code was more useful than I expected. Specific, not generic." — Thanachai K.
// the challenge
Needing More Structure
Wanwisa had done the ML Projects course and wanted to continue developing but struggled with self-direction. She had some experience but felt she needed more support to keep moving forward than a standalone course provided.
// the path
Cohort Mentorship Track
After the pre-enrolment conversation, she joined a small cohort of six learners. The weekly modules gave her a consistent schedule, and the mentor sessions helped her work through specific points where she'd got stuck or wasn't sure if her approach was reasonable.
// after finishing
Deeper, More Confident Understanding
By the end of the programme she had a more developed understanding of both the technical and the practical sides of ML work. She described the experience as demanding but worth it — particularly the combination of structured modules and personal feedback.
"It's not easy, but the difficulty is the point. I feel like I actually understand what I'm doing now." — Wanwisa T.
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