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

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

• Linear, Logistic regression
• Naive Bayes
• SVM / Kernel–
• Random Forests, decision Trees, Boosting, Bagging, Xgboost- StatQuest Youtube videos
• EM Algorithm
• K means
• K nearest neighbors
• Evaluation Metrics (scroll to the definition section, you need to know the confusion metrics, precision, recall, type I, type II, FP rate, sensitivity)
• Regularization (L1,L2, Why is L1 sparse?)
• Bias Variance Trade off
• Dimensionality Reduction
  • PCA deep learning book chapter 2 (last pages)
  • TSNE

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.

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