1. What’s the trade-off between bias and variance?
  2. What is the difference between supervised and unsupervised machine learning?
  3. How is KNN different from k-means clustering?
  4. Explain how a ROC curve works.
  5. What is Bayes’ Theorem? How is it useful in a machine learning context?
  6. Why is “Naive” Bayes naive?
  7. Explain the difference between L1 and L2 regularization
  8. What’s your favorite algorithm, and can you explain it to me in less than a minute?
  9. What’s the difference between Type I and Type II error?
  10. What’s the difference between probability and likelihood?
  11. What is deep learning, and how does it contrast with other machine learning algorithms?
  12. What cross-validation technique would you use on a time series dataset?
  13. How is a decision tree pruned?
  14. Which is more important to you– model accuracy, or model performance?

TOP MNC MACHINE LEARNING INTERVIEW QUESTIONS