What Will You Learn?

• Topics Covered:
• Data Science Interview Questions:
• 1. How to Handle missing value imputation for quantitative values?
• 2. How will you compare 2 quantitative variables?
• 3. What is the DER model? And what are the methods dependent on it?
• 4. What is forward regression?
• 5. What is stepwise regression?
• 6. What is VIF and in what rage it varies?
• 7. What is fisher's exact test?
• 8. What is a correlation, and are its measures?
• 9. What are the types of simple and multiple regression?
• 10. What do you mean by Mean Squared error?
• 11. Is R-squared goodness-of-fit?
• 12. What is the difference between RMSE and R squared in statistics?
• 13. What is the state-of-the-art technique in pattern recognition and machine learning?
• 14. What is the difference between general linear models and generalized linear models?
• 15. What is the best application of linear regression?
• 16. What is the assumption of Linear Regression?
• 17. What are the consequences of violating linear regression assumptions?
• 18. What are the limitations of linear regression modeling in data analysis?
• 19. How is least squares different from linear regression analysis?
• 20. How is hypothesis testing used in linear regression?
• 21. Can you use a categorical variable in linear regression?
• 22. What are the advantages and disadvantages of linear regression?
• 23. What’s the linear regression slope formula?
• 24. Why is logistic regression considered a linear model?
• 25. What are the advantages of logistic regression?
• 26. What are the disadvantages of logistic regression?
• 27. How do you interpret concordance in logistic regression?
• 28. What does the bias term represent in logistic regression?
• 29. Is there an error term in logistic regression?
• 30. Why is collinearity a problem for logistic regression?
• 31. What is the C Parameter in logistic regression?
• 32. How is predictive modeling used in logistic regression?
• 33. Which feature selection methods are better in logistic regression?
• 34. Why is logistic regression so important for machine learning?
• 35. Does scikit-learn support ordinal regression?
• 36. How can you improve the precision/recall of a logistic regression model?
• 37. What is log-likelihood in logistic regression?
• 38. How would you evaluate a logistic regression model?
• 39. What is the difference between neural networks and logistic regression?
• 40. Does logistic regression require the independent variable to be normally distributed?
• 41. How do I deal with missing data when running a logistic regression?
• 42. What is SVM?
• 43. Write some real-life applications of SVM?
• 44. What are the advantages of SVM algorithms?
• 45. Which is the best kernel for SVM and Why?
• 46. Why does XGBoost perform better than SVM?
• 47. What is a multi-class SVM Method?
• 48. How does an SVM choose its support vectors?
• 49. What is the disadvantage of structural SVM?
• 50. What is the best way to train an SVM on a very large dataset?
• 51. What is the difference between the perception learning algorithm and SVM?
• 52. What is the difference between SVM Rank and SVR (support vector regression)
• 53. How many kinds of SVM algorithms exist?
• 54. What is the difference between the normal soft margin SVM and SVM with a linear kernel?
• 55. How is a linear classifier relevant to SVM?
• 56. Is one class SVM really unsupervised?
• 57. How can you choose the parameter C for SVM?
• 58. In layman’s terms, how does Naive Bayes work?
• 59. Why is “naïve Bayes” naïve?
• 60. What are the advantages of using a naïve Bayes for classification?
• 61. Are Gaussian Naïve Bayes the same as binomial Naïve Bayes?
• 62. What is the difference between the Naïve Bayes Classifier and the Bayes classifier?
• 63. In What real-world applications in Naïve Bayes classifier used?
• 64. Is naive Bayes supervised or unsupervised?
• 65. When is Naive Bayes better than logistic regression