What Students Say About the Courses
Unedited accounts from learners who completed Synapsy courses. Some are positive, some note things that were harder than expected — both are useful.
Back to HomeFrom Learners Who Completed the Courses
Reviews collected from students who finished at least one full course. Dates are when the course was completed.
Wanida Sriprasert
Bangkok · Python Foundations
"I had tried to teach myself Python twice before and given up both times. The weekly notebooks made a real difference — having something concrete to do each week kept me from drifting. The written feedback on my third submission was the most useful thing in the course. The educator pointed out that I was writing loops where I could use list comprehensions, and explained why. I understood it immediately when it was shown in my own code."
completed April 2025
Kitti Phanthanawibul
Chiang Mai · ML Pathway
"The ML Pathway was harder than I expected in weeks five and six, when feature engineering and cross-validation came together. I had to go back and re-run the earlier notebooks before I felt like I understood what was happening. The office hours were genuinely useful — I asked a question about why my validation score was so different from my training score and got a clear explanation rather than a link to a documentation page. The third portfolio project took me longer than the estimated time, but I learned more from it than from the first two combined."
completed March 2025
Nattaporn Thongsuk
Bangkok · Python Foundations
"I work full-time in logistics and was worried the course would be too much to manage alongside a desk job. Four to six hours a week turned out to be realistic — I would do two hours on Wednesday evenings and the rest on Saturday morning. The self-paced structure meant I could catch up if a work week was busy without falling behind in a fixed cohort schedule. The feedback on my notebooks was more specific than I expected, which helped."
completed April 2025
Prawit Wichianchan
Phuket · Deep Learning Bootcamp
"The bootcamp is genuinely intensive. I had done the Python and ML courses here first, which helped, but the deep learning weeks were still a step up. The first mentor code review came at a good point — I had written something that ran correctly but was inefficient, and seeing it explained in the review made me rethink how I was approaching the problem. The capstone panel was more demanding than I expected. They asked questions I had to think about carefully. The experience of having to explain my choices out loud, rather than just submitting a file, was worth the discomfort."
completed March 2025
Siriporn Laohakul
Bangkok · ML Pathway
"I took the ML Pathway after finishing Python Foundations here, and the transition felt natural. The concepts built on each other in a way that made sense rather than jumping to new topics without connection. I did find weeks nine and ten dense — the evaluation metrics section covers a lot in a short time and I had to spend extra hours on it. That said, I came out of the twelve weeks with three projects I can actually describe in detail, which feels different from finishing a course and having a certificate but nothing concrete to show."
completed May 2025
Anon Kongkaew
Khon Kaen · Python Foundations
"I am based outside Bangkok and studied entirely online. The pre-enrolment response from the school was helpful — I sent a message describing my background (some basic HTML and spreadsheet skills, no programming) and received a straightforward answer saying the Python Foundations course was appropriate. That kind of direct response before spending money is something I appreciated. The course itself matched the description on the website, which is not always the case with online learning."
completed April 2025
Three Learners in More Detail
Longer accounts of how three students approached the courses and what they took from them.
Data analyst, 3 years experience
Worked in Excel and SQL daily but had not written Python. Wanted to understand what colleagues in the data science team were doing when they built models, rather than being handed outputs to interpret.
Python Foundations → ML Pathway
Took Python Foundations at roughly five hours per week while working full-time, then continued to the ML Pathway six weeks after finishing. Found the progression from one course to the next smooth because the vocabulary introduced in the first course was assumed in the second.
Better equipped for cross-team work
Finished the ML Pathway with three portfolio projects and a clearer understanding of how models are evaluated. Reported being more useful in discussions about model performance metrics in team meetings. Did not change roles but describes the work as more comprehensible.
Software developer, backend focus
Comfortable with programming in Java and Go. No background in Python or statistics. Wanted to understand neural network architectures well enough to evaluate whether deep learning approaches made sense for problems encountered in work.
ML Pathway → Deep Learning Bootcamp
Skipped Python Foundations due to existing programming experience. Took the ML Pathway to build statistical foundations before the Bootcamp. Spent around eight hours per week on the ML course and closer to ten on the Bootcamp.
Capstone project on text classification
Completed the Bootcamp capstone — a text classification system trained on a Thai-language dataset. The panel presentation required explaining architectural choices and trade-offs in detail. Described the second mentor code review as the point where the training process started to make sense at a mechanical level.
University student, second year
Studying economics at a Bangkok university. Had taken one introductory Python course at university but felt the material had moved too quickly. Wanted to cover the same ground at a calmer pace with more hands-on practice.
Python Foundations
Took Python Foundations over the university summer break. Studied at roughly four hours per week, using the flexibility to spread work across the week rather than doing long study sessions. Found the written notebook feedback more useful than the automated grading used in university assessments.
Stronger foundation for university work
Returned to university in the autumn with a clearer understanding of Python fundamentals. Found economics data assignments more approachable as a result. Plans to take the ML Pathway after finishing the academic year, with the intention of applying machine learning methods in an undergraduate research project.
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Synapsy in Numbers
Data as of May 2025. Rating is the average across all submitted post-course surveys.
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