Describing my previous experience, ML theory (some of which was fundamental stuff which I struggled to recall, but most of it was straightforward and what you'd expect from an ML theory interview), and walking through a case study with the interviewer (presented with a modelling opportunity, asked what things I would need to consider and walking through the steps to get the ML model over the line).
They are very interested in that besides pure machine learning knowledge you also understand the broader business context (i.e. how and where ML can solve business problems for Deliveroo)
What's your opinion on working >40 hours a week (rather 50-60, 40 being an exception)
Difference between linear and logistic regression. Difference between lasso and ridge regression. Bagging, boosting, forms of regularisations in algorithms. What is the operation behind convolution.
DSA questions, with sliding window
What is your experience in ML?
About resume, nothing difficult.
Regression explain with different problems
Data science take home case study.
Can you provide some time slots for next week so we can schedule the final interview?
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