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#1. What is Data Science?
#2. Which programming languages are commonly used in Data Science?
#3. What is the purpose of exploratory data analysis (EDA) in Data Science?
#4. What is the main goal of data preprocessing in the context of machine learning?
#5. Which of the following is used for feature selection in machine learning?
#6. What does the term “overfitting” mean in the context of machine learning?
#7. What is a confusion matrix used for in the evaluation of classification models?
#8. What is the purpose of cross-validation in machine learning?
#9. Which algorithm is commonly used for both classification and regression tasks in machine learning?
#10. What is the primary purpose of regularization techniques in machine learning?
#11. What is the difference between supervised and unsupervised learning in machine learning?
#12. What does the term “feature engineering” refer to in the context of machine learning?
#13. What is the main goal of clustering algorithms in unsupervised learning?
#14. What is the purpose of dimensionality reduction techniques like PCA (Principal Component Analysis) in machine learning?
#15. In data preprocessing, what is imputation used for?
#16. What is the primary purpose of the term frequency-inverse document frequency (TF-IDF) in text mining and natural language processing?
#17. What does the term “precision” represent in the context of classification models?
#18. Which algorithm is commonly used for anomaly detection in data science?
#19. What is the purpose of ensemble methods in machine learning?
#20. Which metric is commonly used for evaluating regression models in data science?