Where our graduates ended up

I hadn't touched math since school and was terrified of the deep learning stage. The spine layout meant every week actually made sense. Six months later I'm training models for a living.

My degree taught theory but I couldn't build anything. The agentic AI capstone became the centerpiece of my portfolio — the interviewer spent the whole call asking about it.

I could already code, so I skimmed the early stages and went deep on MLOps and deployment. Being able to actually ship a model to production is what earned me the promotion.

Studying at night around a full-time job felt impossible until the mentorship kicked in. My mentor reviewed every project and pushed me until it was interview-ready. Worth every hour.

Coming from a non-technical field, the statistics stage was the bridge I didn't know I needed. By the AI concepts weeks, transformers finally clicked instead of feeling like magic.

The agentic workflows module was unlike anything in other courses I'd tried. I built an internal automation agent at work using exactly what I learned, and it got noticed fast.

I was skeptical a career switch at 34 was realistic. The structured path removed the guesswork — I always knew exactly what to study next. That clarity is what kept me going.

The deep learning projects gave me real CNNs to show, not toy notebooks. I walked into my interview with a working demo on my phone and got the offer the same week.

I knew SQL and dashboards but wanted to build the models, not just report on them. The ML stage filled every gap, and the evaluation module made me genuinely rigorous about results.