1.What is machine learning, and how is it different from traditional programming?

2.What are the main types of machine learning algorithms?

3.Explain the difference between supervised, unsupervised, and reinforcement learning.

4.What is the bias-variance trade-off in machine learning?

5.How do you handle missing data in a dataset?

6.What is cross-validation, and why is it important in machine learning?

7.What is overfitting, and how do you prevent it?

8.Can you explain the ROC curve and AUC (Area Under the Curve)?

9.Describe the k-nearest neighbors (KNN) algorithm.

10.How does a decision tree work, and what are its advantages and disadvantages?

11.Explain the concept of gradient descent and its role in training machine learning models.

12.What are support vector machines (SVMs), and when are they useful?

13.What are neural networks and how do they learn?

14.What is the vanishing gradient problem in neural networks?

15.What is transfer learning, and how can it be used to improve model performance?

16.How would you handle a dataset with a class imbalance problem?

17.Describe the term "hyperparameter tuning" and its significance.

18.What are the different evaluation metrics used for regression and classification tasks?

19.Explain the concept of regularization in machine learning.

20.What are the advantages and disadvantages of ensemble learning methods?