Prajya
Prajya Learners

Learner Stories

What Our Learners Say

Honest accounts from people who have worked through our programmes.

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240+

Learners Enrolled

4.7

Average Rating

3+

Years Running

88%

Completion Rate

Reviews

From the Learners Themselves

SP

Siriporn Phongkham

Bangkok Β· Foundations of ML

I'd tried a couple of those big online platforms before and always got stuck around week three. The Prajya foundations course was different β€” things moved at a pace I could actually follow, and when I emailed a question about cross-validation, I got a proper reply the next morning. The exercises are practical, nothing just theoretical.

June 2025

WT

Wichai Tirawat

Chiang Rai Β· Computer Vision Track

The computer vision track taught me more in eight weeks than six months of reading docs on my own. Krit has a way of explaining convolutional layers that finally made it click for me. A few of the early exercises were a bit challenging if you're rusty on numpy, but the instructor caught that and sent extra notes. Good value for the price.

June 2025

NC

Nattaya Chaipreeda

Chiang Mai Β· Capstone Mentorship

I enrolled in the mentorship programme with a rough idea for a project β€” classifying satellite images of agricultural land. Nara helped me scope it down to something actually doable in twelve weeks, and the weekly sessions were genuinely useful. I finished with a working model and a clear sense of what to do next. Worth every baht.

July 2025

PT

Parita Thamrongsak

Hat Yai Β· Foundations of ML

I work in healthcare administration and had been curious about how data science might apply to what I do. The foundations programme gave me a realistic picture of both the possibilities and the limitations β€” which I appreciated. The instructor didn't oversell it. Materials were clear and I never felt lost.

July 2025

AW

Arthit Wongprasert

Khon Kaen Β· Computer Vision Track

I came in as a software developer who understood Python but had never worked with image data. The track built up well β€” from basic image manipulation right through to training a small classification model. The pace felt right. I appreciated that the exercises didn't just hand you the solution to copy.

June 2025

MN

Manisa Noppakun

Phuket Β· Capstone Mentorship

The mentorship programme suited me because I needed to work around a busy schedule. We shifted a session twice when things came up at work and there was no issue β€” just a calm rearrangement. The feedback on my project write-ups was genuinely detailed, not just a thumbs-up. I'd recommend Prajya to anyone who learns better with actual human contact.

July 2025

Learner Journeys

A Closer Look at Three Learner Paths

Case Study 01

From Biology Teacher to ML Practitioner

The Challenge

A secondary school biology teacher in Chiang Rai wanted to understand how machine learning tools could apply to ecological data analysis. She had no programming background and found most resources either too basic or too technical to follow.

The Approach

She enrolled in the Foundations programme and worked through it over seven weeks at a pace that suited her teaching schedule. The exercises were tied to real datasets, including one involving plant species classification, which matched her subject knowledge.

The Outcome

By the end of the programme she had built and evaluated three small classification models, and had a clear understanding of when and how ML tools might be useful in her own research. She later enrolled in the Mentorship Programme to go further.

Case Study 02

Building a Quality Control Tool for a Small Manufacturer

The Challenge

A junior developer working at a small ceramics manufacturer in Lampang needed a way to detect surface defects in production photos. He had basic Python skills but no experience with computer vision.

The Approach

He completed the Computer Vision Track and brought his own image dataset from the factory floor. His instructor helped him adapt the exercises to defect detection rather than the generic examples provided. The track took nine weeks given the added scope.

The Outcome

He completed a working prototype that flagged approximately 80% of defective pieces in testing β€” a starting point that his employer is now looking to develop further. He described the programme as "much more relevant than I expected."

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Address

137 Nimman Soi 11
Chiang Mai 50200

Office Hours

Mon–Fri: 09:00–18:00
Sat: 10:00–14:00 (ICT)

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