What People Say After
Going Through the Courses
These are accounts from people who have completed one or more Cogito courses. We include both what worked and what they found difficult.
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From Learners Across the Courses
Apinya Thongmee
Chiang Mai, Thailand
I finished the Starter Course in about seven weeks — slightly slower than the suggested pace because I was working full-time. The written materials made that possible; I could read at my own speed and come back to sections that weren't clear. The weekly Q&A was the most useful part for me. Having somewhere to ask about things I'd been stuck on made a real difference.
Starter Course · May 2025
Rathana Khem
Phnom Penh, Cambodia
Machine Learning in Practice was harder than I expected — the data preparation sections took me several passes. But that's probably realistic for the subject. What I appreciated was that the course didn't pretend there was an easy version. The mentor feedback on assignments was genuinely useful; it pointed out things in my code that I hadn't noticed, with explanations of why they mattered.
Machine Learning in Practice · April 2025
Somporn Wiratham
Bangkok, Thailand
I did the AI Systems Track after finishing Machine Learning in Practice. The fourteen weeks felt long at the start, but by the midpoint I could see why the pace was set the way it was — the project builds on itself and you need time to get the earlier parts right before adding complexity. I have the portfolio project on GitHub now and it's been useful for describing what I can do.
AI Systems Track · March 2025
Mei-Ling Chan
Kuala Lumpur, Malaysia
The Starter Course does exactly what it says. I had tried two other online Python courses before and stopped both because they moved to complex topics before I understood the simpler ones. Cogito's version is slower and more thorough. The exercises are well-designed — they're not repetitive, but each one tests something specific.
Starter Course · May 2025
Jakub Petrowski
Warsaw, Poland
I took Machine Learning in Practice while travelling. The self-paced structure worked well — I could do intensive study one week and very little the next depending on where I was. The recorded walkthroughs were helpful for the more technically detailed sections. I'd have liked slightly more worked examples in the model evaluation part, but the Q&A sessions covered what I was missing.
Machine Learning in Practice · April 2025
Nattaporn Krisana
Chiang Rai, Thailand
The AI Systems Track changed how I think about building software with ML components. Before the track I could train models but not really think about what comes after. The deployment section made that concrete. The 1:1 sessions were the highlight — having an hour with a mentor who had looked at my code beforehand was much more useful than office hours with 20 other people waiting.
AI Systems Track · April 2025
Learner Journeys in Detail
Three accounts of what learners came in with, what they worked on, and where they are now.
Apinya worked in data entry and wanted to move into data-related work but had no programming background. She'd tried YouTube tutorials twice but found them hard to follow without a structured order.
Started with the Starter Course, which she completed over seven weeks. Moved to Machine Learning in Practice four months later. Used the Q&A sessions each week to address specific points she found unclear.
After completing both courses she started working on a small side project, analysing transport data for her local area. She describes the courses as having given her a vocabulary for problems she could already see but couldn't previously articulate.
"I knew what I wanted to be able to do — I just didn't know where to start. The Starter Course gave me that."
Somporn had a software engineering background and could write Python but had tried to self-teach machine learning from documentation and found the concepts didn't connect well without worked examples and someone to ask.
Joined Machine Learning in Practice, skipping the Starter Course. Over ten weeks he worked through model evaluation and handling imbalanced datasets, areas that had previously been unclear from documentation alone.
Enrolled in the AI Systems Track the following cohort. The portfolio project he completed is an API-based service that runs a simple classification model. He uses it in conversations with employers to explain what he built and how.
"The feedback on my assignments was the thing that actually moved me forward. It pointed to exactly what I was getting wrong."
Jakub had studied statistics at university and understood the theory behind many ML concepts but hadn't put them into practice in code. He found existing courses either too basic or too fast.
Took Machine Learning in Practice while travelling for several months. Used the recorded walkthroughs during quieter weeks and attended Q&As when his schedule allowed. Submitted all assignments.
Finished the course with a working understanding of practical data preparation and model evaluation that he hadn't had before. Plans to take the AI Systems Track when he's in a more stable location.
"I finally have a version of machine learning I can actually use rather than just describe."
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