Top 11 Popular Machine Learning Interview Questions and Answers in 2021

Top 11 Popular Machine Learning Interview Questions and Answers in 2021

Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions in 2021: Q1. What are different types of Machine Learning, and briefly explain them? Q2. Give me an example of supervised learning and another for unsupervised learning? Q3. You built a DL model, and while training it, you noticed that after a certain number of epochs, the accuracy is decreasing. What’s the problem and how to fix it? Q4. What’s the difference between Bias and Variance in DL models? How to achieve a balance between them? Q5. What’s the confusion matrix? Is it used for both supervised and unsupervised learning? What are Type 1 and Type 2 errors? Q6. What is a model learning rate? Is a high learning rate always good? Q7. What vanishing gradient descent? Q8. What’s the difference between KNN and K-means? Q9. What does it mean to cross-validate a machine learning model? Q10. How to assess your supervised machine learning model? What’s Recall and Precision? Q11. What’s the Curse of Dimensionality, and how to solve it?

Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions in 2021

Interviews are hard and stressful enough and my goal here is to help you prepare for ML interviews. This list is not conclusive of all interview questions nor guaranteed to help you pass the interview. It’s basically a list of questions I gathered from sitting on many interviews as an interviewer.

  • Q1. What are different types of Machine Learning, and briefly explain them?
  • Q2. Give me an example of supervised learning and another for unsupervised learning?
  • Q3. You built a DL model, and while training it, you noticed that after a certain number of epochs, the accuracy is decreasing. What’s the problem and how to fix it?
  • Q4. What’s the difference between Bias and Variance in DL models? How to achieve a balance between them?
  • Q5. What’s the confusion matrix? Is it used for both supervised and unsupervised learning? What are Type 1 and Type 2 errors?
  • Q6. What is a model learning rate? Is a high learning rate always good?
  • Q7. What vanishing gradient descent?
  • Q8. What’s the difference between KNN and K-means?
  • Q9. What does it mean to cross-validate a machine learning model?
  • Q10. How to assess your supervised machine learning model? What’s Recall and Precision?
  • Q11. What’s the Curse of Dimensionality, and how to solve it?

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