1. What are the main differences between classification and regression algorithms?
2. Explain the concept of regularization and its importance in regression.
3. What are the different types of regularization techniques in machine learning?
4. How does Lasso regression differ from Ridge regression?
5. What is the purpose of a validation set in machine learning?
6. Explain the difference between bias and variance in model evaluation.
7. What are ensemble methods, and why are they useful?
8. How does a random forest algorithm work?
9. What is feature importance, and how is it calculated?
10. Explain the difference between bagging and boosting.
11. What is the purpose of hyperparameter tuning in machine learning models?
12. How do you choose the number of clusters in k-means clustering?
13. What is the silhouette score, and how is it used in clustering?
14. Explain the concept of dimensionality reduction and its significance.
15. What is the purpose of PCA (Principal Component Analysis)?
16. How does the k-nearest neighbors (KNN) algorithm work?
17. What are the advantages and disadvantages of KNN?
18. What is cross-validation, and why is it important?
19. Explain the concept of ROC-AUC and its significance in model evaluation.
20. What is a precision-recall curve?
21. How do you handle class imbalance in a dataset?
22. What is SMOTE, and how does it help with imbalanced datasets?
23. Explain what a confusion matrix is and how to interpret it.
24. What is the F1-score, and when is it most useful?
25. What are the different types of machine learning algorithms?
26. What is the difference between supervised and unsupervised learning?
27. Explain the concept of reinforcement learning.
28. What are the components of a reinforcement learning problem?
29. What is Q-learning in reinforcement learning?
30. How do you evaluate the performance of a reinforcement learning model?
31. What is a neural network, and how does it work?
32. Explain the structure of a feedforward neural network.
33. What is backpropagation, and how does it work?
34. What are activation functions, and why are they important?
35. What is the difference between convolutional neural networks (CNNs) and recurrent neural networks (RNNs)?
36. How does a convolutional layer function in a CNN?
37. What is the purpose of pooling layers in CNNs?
38. What is transfer learning, and how can it be beneficial?
39. Explain the vanishing gradient problem and its implications.
40. What is dropout, and how does it help prevent overfitting?