Can you explain the difference between supervised and unsupervised learning? How do you handle overfitting in a machine learning model? Describe a machine learning project you’ve worked on and its biggest challenges. What are the steps you follow for feature engineering? How do you evaluate model performance? What metrics do you use? Can you explain regularization techniques like L1 and L2? How would you scale a machine learning model for large datasets? What’s the importance of hyperparameter tuning, and how do you approach it? Describe the difference between bagging and boosting. How do you deploy machine learning models in production?
