General interview questions regarding how the Classification metrics like F1, AUC etc can be enhanced by giving a small case study with dataset and baseline confusion metrics. Binary and Multi Class Classification algorithms and a few questions regarding the gradient descent algo training times from problem statement to statement
ML and coding questions. Shallow and basic ML questions. Coding questions were easy, except for one which was very hard (difficulty level was Expert in geeeksforgeeks) and the interviewer didn't make any efforts to explain it properly.
1- resume based questions, especially former projects
Tell me about one of the hardest problems you solved
I wasnt too prepared for the interview. I was asked questions on XGBoost, Logistic regression, some stat concepts, some drilling into NLP algorithms. Finally, there was a some drilling into web-visualization type questions using SVG etc. Overall, the questions werent super hard but I felt underprepared.
just a technical screening interview..
2 probability calculation questions using conditional prob and bayes thm, statisics questions from all around, classification model questions, medium sql query
Typical behavioral and deep dive in technical projects with questions focused on ML design.
In the last round, they didn't even discuss the conclusion presented.
What is p-value ? Definition and your understanding.
Viewing 1621 - 1630 interview questions