What People Who've Done the Programmes Say

"I'd tried two other online Python courses and given up both times. The Foundations programme was different because the pacing actually made sense — and having a real person look at my code and tell me specifically what I'd misunderstood made a big difference. It took effort, but I got through it."

"The ML Engineering track is not easy — which is actually what I wanted. I work in operations for a logistics company and wanted to understand the ML tools our data team uses. The projects used familiar types of data (delivery times, demand patterns) which made it easier to see why each step mattered."

"I appreciated that the mentors were direct about what the programme covers and what it doesn't. There were no claims about what I'd be able to do afterwards — just a clear description of the curriculum. The capstone project genuinely stretched me, and having Arjun's input during the 1-to-1 sessions was worth a lot."

"I did the Foundations programme while working full-time. The sessions are recorded, which saved me several times when I had to work late and couldn't attend live. The exercises took me about 8–9 hours a week in total, which I'd managed to fit around my schedule by week 3."

"The code review feedback was honest — probably more honest than I was expecting. On one submission I got told clearly that my approach worked but was harder to read than it needed to be. That kind of feedback is what I was hoping for. The programme itself moved at a pace that felt right for the level."

"I enrolled in the Advanced programme after completing the ML Engineering track here. Having the same team and a consistent teaching style across both made the transition easier than starting somewhere new. The deployment section was the most practical AI content I've come across — actually useful for what I want to build."