1. Explain the concept of overfitting in machine learning and how it can be addressed. 2. Discuss the differences between supervised and unsupervised learning algorithms. 3. How would you handle imbalanced datasets in machine learning projects? 4. Describe the process of feature selection and its importance in model development. 5. Have you worked with any deep learning frameworks? If so, which ones and what was your experience like? 6. How do you evaluate the performance of a machine learning model? 7. Can you explain the bias-variance tradeoff and its significance in model training? 8. Discuss regularization techniques in machine learning and when they should be used. 9. Have you implemented any natural language processing (NLP) algorithms? If yes, explain the approach and challenges faced. 10. How would you approach a project with limited labeled data for training a machine learning model?
