What people say after studying here

I had tried a couple of online AI courses before and gave up partway through both. The gap between watching a video and understanding what I was supposed to do next was just too wide. Here it was different — the tasks were clear enough that I could actually attempt them. My mentor caught a misconception I had about how training data works and explained it in a way that actually made sense. I finished the portfolio project feeling like I had built something, not just followed instructions.

The machine learning course was dense but I appreciated that it did not skip past the harder parts. When I got stuck on evaluation metrics — specifically on understanding when accuracy is a misleading measure — I sent a message and got a response the next day with a concrete example I could run myself. The group sessions were useful too, mostly for hearing what questions other people were asking. I would say allow yourself a bit more time than you expect for the later sections.

I came in with some Python and basic ML knowledge and enrolled directly in the engineering track. The monitoring and deployment sections were the most valuable for me — those are topics I had read about but never actually worked through. My mentor had clear opinions about what mattered in production versus what was just theory, and those opinions showed up in the code review notes in a helpful way. The portfolio project took me longer than I thought but I am pleased with what I ended up with.

Honestly I was not sure I could follow the material — I work in accounting and do not have a technical background at all. The first few weeks were slow but I was not lost. The explanations before each task helped a lot. My mentor was patient with basic questions and I never felt embarrassed asking them. I think I covered more ground in eight weeks than I expected to. Now I am thinking about enrolling in the machine learning course next year.

The thing I keep telling people is that the feedback is what you are paying for. Krit reviewed my portfolio piece and the notes were two pages long, in the best possible way. He flagged things that worked, things that would be a problem in a real deployment, and pointed me toward one paper to read that changed how I thought about the problem I had been solving. That kind of attention is what you do not get from a platform that has ten thousand students enrolled at once.

I had been self-studying AI for about a year before joining, piecing things together from tutorials and documentation. What Chalerm added was structure and a person to tell me when I was going in the wrong direction. I expected the responsible AI sections to feel like box-ticking but they did not — they came up in context, in tasks where the choice I made would have a downstream effect on people. That was the part I learned the most from. Still working through the engineering track at the moment.