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Interview Resources for Machine Learning, Data Science, AI Research Engineers

ML/Data Science/AI Research Engineer

ARTICLE SUMMARY

Purvanshi Mehta, Data Scientist at Microsoft writes a curated list of topics, resources and questions that will help you pass Machine Learning Interviews.

Read our curated list of topics, resources, and questions.

Interviewing is a grueling process, especially during COVID. I recently interviewed with Microsoft (Data Scientist ll), Amazon (Applied AI Scientist), and Apple (Software Development: Machine Learning).

Though all these interviews differed a bit, the basic questions asked were the same. During the process, I curated this list which would help you pass all ML interviews.

NOTE: This list is just for end moment revising


Machine Learning

MACHINE LEARNING

Deep Learning

The first thing I would suggest to do is to go through all the deeplearnig.ai courses which are pretty basic. If someone already publishes/ works in these topics they might just skip watching all the videos and can go through the following questions/ resources

  • Know what is- K fold cross-validation, dropout, batch norm [Difference between batch norm and layer norm], early stopping
  • Weight decay
  • Calibration (Look to what is calibration and ECE score)
    Transformer
  • Attention (Multi-head, single head) Different Optimizers (Important are- Gradient descent, Adam, RMSprop, Adagrad, Adamax)
  • Initialization

NLP

For NLP CS224 covers the basics of NLP with Deep Learning. This might cover 3/4 of the questions asked in an interview. Other questions are usually more state-of-the-art models as the interviewer wants to check how updated you are.

LSTM, GRU

Different types of embeddings (Bag of words, TFIDF, Word2vec(skipgram[How is it trained], pre-trained (Google word2vec, Stanford Glove, fasttext, ELMo))). Need to know how incremental changes were brought into place.

Other topics

  1. Linear Algebra
  2. Probability basics
  3. Stats I had taken a graduate-level Statistics class so I didn’t need to brush this up but Khan Academy is a very good source for learning basics with examples.

These are the topics which are asked in all interviews, obvious then some questions were specific to research I had done. There were also live coding rounds both of algorithms and NN models. Let me know if I missed something.

 Purvanshi Mehta

Thank you to Purvanshi Mehta for letting us publish her blog. You can follow Purvanshi on Medium

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