Companies rely on machine learning engineers to help design and improve the systems that allow their software to improve on its own, rather than being specifically programmed. During the interview process, be prepared to be tested heavily on both computer science and data science knowledge with an emphasis on recognizing patterns and trends. A bachelor's degree in computer science or a related field will be required.
(Coding) Write an API & data structure for a specific use case (how would you test it, what's the complexity of the different operations...)? What's your experience with feature engineering? In hindsight, how would you do it differently and why? What's your experience interacting with product teams/managers?
1- Given n points, find the max number of point that lie on the same line. 2- Simple mapreduce task. 3- Feature selection.
Self Introduction Master Thesis NLP Questions
Behavioral questions, like tell me about something you build or a conflict in a workplace.
What are your ideas and how can you innovate what we want to achieve with the help of Machine Learning an Data Science?
Why clearstream? Tell me about yourself and your background. Technical questions Tell about scikit learn, pandas and your experience with python and ml
how to get bell curve from binomial distribution
Why did you apply to this program?
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.
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