What people say after finishing a course

Manida had been working with Excel models for five years and wanted to understand why her colleagues in data were spending time on Python when the spreadsheet did the same job. She came to the Getting Started course with no coding background and a degree of scepticism about whether it was actually necessary.

Twelve weeks into the Getting Started course she had a working model that predicted loan default risk using a public Thai banking dataset — something her Excel workflow could not have approached. She enrolled in Practical ML Projects three months later, after using her new Python skills on three separate projects at work.

Manida has since been reassigned to a cross-functional team that includes data engineers. She says the main change is that she can now participate in technical conversations rather than waiting for summaries. She is considering the Full-Stack Track.

Jakrapan had been writing web applications for several years and wanted to add AI capabilities to his work — specifically, to integrate language models into products he was building. He came in with strong Python and a solid understanding of APIs, but no experience with model training or ML infrastructure.

The Full-Stack Track gave him the ML foundations he had been missing, then moved into deployment and LLM API integration — which aligned directly with what he wanted to do commercially. The portfolio project he built was a document question-answering system using a retrieval-augmented generation approach.

Jakrapan now works independently on AI-enhanced web products. He says the portfolio project, with its documentation, has been more useful in conversations with prospective clients than any credential he could have shown. He references the course materials regularly.

Pornthip managed a logistics team and had been asked to evaluate whether AI tools could improve forecasting in her department. She had no technical background and was not certain the Getting Started course was aimed at someone in her position. She called the studio before enrolling and spoke with Siriporn about whether it made sense for her.

She completed the twelve-week course while managing her team full-time, studying mostly on weekends and two evenings per week. Her final project was a simple demand forecasting model using historical order data from her company. She described the experience as slower than she expected, in a way that was actually useful.

Pornthip is no longer the person in the room who cannot evaluate a technical proposal. She says her main gain was not the code itself, but the ability to ask useful questions about AI tools vendors bring to her team — and to understand when the answers make sense.