I won't give details about the question as I respect the confidentiality of the interview. However, to give a general feeling, I think it doesn't hurt to mention the following. For example, code a class that implements a very popular ML algorithm. Even if the algorithm is very simple there are lots of possible improvements and generalisations, how to make it robust, efficient etc. Same thing for a class storing common data formats: dataframe, time-series, etc... how would you efficiently code access methods and/or storing according to the features of these data types?
Machine Learning Interview Questions
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Questions on Custom Kmeans, GenAI, VectorDB, design, approach to solve the real time techincal problems, with scalability, etc.
Q: My profile Q: Why we are discussing Q: Why MLE
Given a certain form of data, asking to write codes to predict the classes.
Q: Detect balanced parentheses (brackets, curly braces, etc) in a string.
Difference between Chess and Candy Crush?
Implement KNN coding and general ML clustering Questions
Given list of intervals as (start, end), where each interval is a task runtime, how many CPUs that can run one task are needed to run all tasks?
Give a brief overview of your projects and history of work.
Two coding interviews and two design interviews and two more behavioral interviews.
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