1) Build a production ready API with an ML model behind. 2) Explain the different optimization algorithms in DNN. 3) What are kernels and what is their use? 4) Why CNN's are translation invariant? 5) Review a Python module suggesting changes. 6) How would you regularize Random Forests? 7) How to make linear regression and Random Forests robust to outliers?
ML and DL basic question, NLP, LLM. Talking about the projects you have done in details.
DSA question was given an array of integers, return another with product of elements except the element at index
AI/ML coding: NLP use case that involves walking through how you would calculate word frequency (TFIDF) then what's the best method to measure vector similarity. **Job description does not mention NLP**
1. Walk me through your experience? What projects did you work on? Hold on, let me interrupt you before you finish your first sentence.
A project on NLP within a week
Behavioral questions, like tell me about something you build or a conflict in a workplace.
Leetcode non-ML questions about strings. In fact, there were no ML coding questions.
Could you please descrive the challenges you had during your recent projects and how you solved them?
What did you do in your previous job? What tools did you use? Did you process large datasets? Do you write code and how do you ensure clean code practices? What are your MLOps capabilities? What problems did your solutions have in your previous work and how did you fix them? They also asked classic ML questions: When is classical machine learning better than deep neural networks? What is boosting, bagging, XGBoost, random forest, and decision trees in general?
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