"To get a job in machine learning, you must have the programming and mathematical knowledge to create artificial intelligence that is capable of learning new tasks without being explicitly coded. In an interview you may be asked about your experience with pertinent coding languages such as Java and C++ as well as with writing algorithms. The interview will be comprised mainly of technical questions that test your knowledge of the fundamental concepts of machine learning such as data mining and signal processing."
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?
They asked me to explain a machine learning system I had worked on end-to-end and justify the design choices.
Could you please descrive the challenges you had during your recent projects and how you solved them?
a linear algebra coding problem (x = ABC given x, B,C find A)
Guide me through a project that used unstructured datasets
Build a movie recommendation engine
asked about python transformer architecture
Did not answer any questions
In a typical epoch, how is GPU usage across each stage. Is Forward pass more GPU intensive than Backward pass?
Ordinary Least Squares question, with 3 non collinear points (1,0) (0,0) and (1,1). Define y intercept and slope.
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