Machine Learning Interview Questions

1. What is Machine Learning?

2. Supervised vs Unsupervised Learning

3. Classification vs Regression in ML

4. What are Overfitting and Underfitting?

5. What is the Bias-Variance Tradeoff?

6. What is Cross-Validation in Machine Learning?

7. What is Feature Engineering?

8. What is Feature Scaling and Normalization?

9. How Does Linear Regression Work?

10. How Does Logistic Regression Work?

11. How Do Decision Trees Work?

12. What is a Random Forest?

13. What is Gradient Boosting?

14. What are Support Vector Machines (SVM)?

15. How Does K-Nearest Neighbors (KNN) Work?

16. How Does K-Means Clustering Work?

17. How Does the Naive Bayes Classifier Work?

18. What is a Confusion Matrix?

19. Precision, Recall and F1-Score Explained

20. What are the ROC Curve and AUC?

21. How Does Gradient Descent Work?

22. What is Regularization (L1 and L2) in ML?

23. What are Ensemble Methods in ML?

24. What is PCA (Principal Component Analysis)?

25. What are Activation Functions in Neural Networks?

26. What are Loss Functions in Machine Learning?

27. What is One-Hot Encoding?

28. How Do You Handle Imbalanced Data in ML?

29. What is Hyperparameter Tuning?

30. What is the Train/Validation/Test Split?

31. What is Supervised vs Unsupervised Learning?

32. What is Overfitting in Machine Learning?

33. Bias vs Variance: What's the Tradeoff?

34. What is Gradient Descent?

35. What is a Confusion Matrix?

36. Precision vs Recall: What's the Difference?

37. What is Cross-Validation?

38. What is Regularization in Machine Learning?

39. What is a Neural Network?

40. What is a Decision Tree?

41. What is a Random Forest?

42. What is a Loss Function in Machine Learning?

43. What Is Feature Engineering in Machine Learning?

44. What Is One-Hot Encoding in Machine Learning?

45. What Is a Learning Rate in Machine Learning?

46. What Is K-Means Clustering and How Does It Work?

47. What Is PCA (Principal Component Analysis)?

48. What Is an Activation Function in Neural Networks?

49. What Is an LLM (Large Language Model)?

50. What Is Fine-Tuning a Machine Learning Model?

51. What Is a Transformer in Machine Learning?

52. What Is Data Annotation in Machine Learning?

53. What Is the F1 Score in Machine Learning?

54. What Is Ensemble Learning in Machine Learning?

55. Why Does a Model That Validates Well Degrade in Production?

56. How Do You Handle Label Noise and Annotator Disagreement?

57. Which Class-Imbalance Strategies Fail, and How?

58. How Do You Run Hyperparameter Search Under a Fixed Compute Budget?

59. When Does Ensembling Stop Improving a Model?

60. How Do You Trade Off Distillation and Quantisation for Serving?

61. How Do You Decide When a Simpler Model Is the Right Answer?