Data science interview questions?

1. What is data science? What would be the important components?
2. Explain three types of learning: supervised learning, unsupervised learning, reinforcement learning.
3. What do you understand by the terms classification and regression in machine learning?
4. How would you handle missing values and outliers in a dataset?
5. What is feature engineering, and why is feature engineering important in machine learning?
6. Explain the bias-variance tradeoff in machine learning and how you would address it.
7. What is cross-validation and why is this employed in machine learning?
8. How do you measure whether a machine learning model has performed well?
9. What is overfitting and how can you prevent it in machine learning?
10. What are a few of the most popular algorithms implemented in supervised learning? Give some examples.
11. Describe the k-means clustering algorithm. What is the way for finding the ideal number of clusters?
12. What is the difference between gradient descent and stochastic gradient descent?
13. How do you choose features for machine learning?
14. Curse of Dimensionality: What it is and what are its impact on machine learning
15. L1 vs L2 regularization in ML
16. What are principal component analyses used for and how do you analyze its results
17. How to overcome class imbalance in dataset?
18. What does an ensemble machine learning method actually do? State with examples:.
19. How do you interpret the results of a confusion matrix?
20. What is deep learning, and how does it differ from traditional machine learning algorithms?