1. Explain the difference between supervised and unsupervised machine learning?
  2. Explain the difference between KNN and k.means clustering?
  3. What is the difference between classification and regression?
  4. How to ensure that your model is not overfitting?
  5. What is meant by ‘Training set’ and ‘Test Set’?
  6. List the main advantage of Navie Bayes?
  7. Explain Ensemble learning.
  8. Explain dimension reduction in machine learning.
  9. What should you do when your model is suffering from low bias and high variance? Explain differences between random forest and gradient boosting algorithm.
  10. What is the "Curse of Dimensionality?"
  11. Explain the Bias-Variance Tradeoff.

TOP MNC MACHINE LEARNING INTERVIEW QUESTIONS