They asked me about my previous research and inquired about numerous technical details related to my work. Details that are not even mentioned in published papers, I wouldn't include. Quite extensive when it comes to the technical literacy of ML/NLP models.
What kind of ML do you know
Questions from my Resume and past projects majorly into credit risk modelling
1) Code up Kmeans, Theoretical Questions regarding ML questions (like Naive Bayes) 2) Find pivot in a rotated sorted array, Lowest common ancestor, Rotate every k elements of a linked list
Describe how the EM algorithm works
What's your experience with long-tail data?
Methods to solve over fitting in deep learning?
math and programming , dsa , projects
ML Concepts: You will be able to answer questions if you have worked in the field. This interview was very interesting to me because it focuses on how you think about the problem and how it can be solved.
Describe how you handle stakeholders. Softball questions.
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