Machine Learning Interview Questions

1. What is Machine learning?
2. Mention the difference between Data Mining and Machine learning?
3. What is ‘Overfitting’ in Machine learning?
4. Why overfitting happens?
5. How can you avoid overfitting ?
6. What is inductive machine learning?
7. What are the five popular algorithms of Machine Learning?
8. What are the different Algorithm techniques in Machine Learning?
9. What are the three stages to build the hypotheses or model in machine learning?
10. What is the standard approach to supervised learning?
11. What is ‘Training set’ and ‘Test set’?
12. List down various approaches for machine learning?
13. What is not Machine Learning?
14. Explain what is the function of ‘Unsupervised Learning’?
15. Explain what is the function of ‘Supervised Learning’?
16. What is algorithm independent machine learning?
17. What is the difference between artificial learning and machine learning?
18. What is classifier in machine learning?
19. What are the advantages of Naive Bayes?
20. What is Inductive Logic Programming in Machine Learning?
21. What is Model Selection in Machine Learning?
22. What are the two methods used for the calibration in Supervised Learning?
23. Which method is frequently used to prevent overfitting?
24. Why instance based learning algorithm sometimes referred as Lazy learning algorithm?
25. What are the two classification methods that SVM ( Support Vector Machine) can handle?
26. What is ensemble learning?
27. When to use ensemble learning?
28. What are the two paradigms of ensemble methods?29. What is the general principle of an ensemble method and what is bagging and boosting in ensemble method?
29. What is bias-variance decomposition of classification error in ensemble method?
30. What is an Incremental Learning algorithm in ensemble?
31. What is PCA, KPCA and ICA used for?
32. What is dimension reduction in Machine Learning?
33. What are support vector machines?
34. Differentiate between inductive learning and deductive learning?
35. What is the difference between Data Mining and Machine Learning?
36. Differentiate supervised and unsupervised machine learning.
37. How does Machine Learning differ from Deep Learning?
38. How is KNN different from k-means?
39. What are the different types of Algorithm methods in Machine Learning?
40. What do you understand by Reinforcement Learning technique?
41. What is the trade-off between bias and variance?
42. How do classification and regression differ?
43. What are the three stages of building the hypotheses or model in machine learning?
44. Describe 'Training set' and 'training Test'.
45. What are the common ways to handle missing data in a dataset?
46. What are the necessary steps involved in Machine Learning Project?
47. Describe Precision and Recall?
48. What do you understand by Decision Tree in Machine Learning?
49. What do you understand by algorithm independent machine learning?
50. Describe the classifier in machine learning.
51. What is SVM in machine learning? What are the classification methods that SVM can handle?
52. What do you understand by the Confusion Matrix?
53. Explain True Positive, True Negative, False Positive, and False Negative in Confusion Matrix with an example.
54. What according to you, is more important between model accuracy and model performance?
55. What is Bagging and Boosting?
56. What are the similarities and differences between bagging and boosting in Machine Learning?
57. What do you understand by Cluster Sampling?59. What do you understand by the F1 score?
58. How is a decision tree pruned?
59. What are the Recommended Systems?
60. When does regularization become necessary in Machine Learning?
61. What is Regularization? What kind of problems does regularization solve?
62. Why do we need to convert categorical variables into factor? Which functions are used to perform the conversion?
63. Do you think that treating a categorical variable as a continuous variable would result in a better predictive model?
64. How is machine learning used in day-to-day life?
65. How Do You Handle Missing or Corrupted Data in a Dataset?
66. How Can You Choose a Classifier Based on a Training Set Data Size?
67. What Are the Applications of Supervised Machine Learning in Modern Businesses?
68. What is Semi-supervised Machine Learning?
69. Compare K-means and KNN Algorithms.
70. What Is ‘naive’ in the Naive Bayes Classifier?
71. How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem?
72. How is Amazon Able to Recommend Other Things to Buy? How Does the Recommendation Engine Work?
73. When Will You Use Classification over Regression?
74. How Do You Design an Email Spam Filter?
75. What is a Random Forest?
76. What is Pruning in Decision Trees, and How Is It Done?
77. Briefly Explain Logistic Regression.
78. Explain the K Nearest Neighbor Algorithm.
79. What is Kernel SVM?
80. What Are Some Methods of Reducing Dimensionality?
81. What is Principal Component Analysis?
82. What do you understand by Type I vs Type II error?
83. Explain Correlation and Covariance?
84. What are Support Vectors in SVM?
85. What is Cross-Validation?
86. What are the different methods to split a tree in a decision tree algorithm?
87. How does the Support Vector Machine algorithm handle self-learning?90. What are the assumptions you need to take before starting with linear regression?
88. What is the difference between Lasso and Ridge regression?
89. What is Entropy in Machine Learning?
90. What is Epoch in Machine Learning?
91. Differentiate between Classification and Regression in Machine Learning
92. How is the suitability of a Machine Learning Algorithm determined for a particular problem?
93. What is ROC Curve and what does it represent?
94. Both being Tree-based Algorithms, how is Random Forest different from Gradient Boosting Machine (GBM)?
95. What do you understand about the P-value?
96. Suppose you found that your model is suffering from high variance. Which algorithm do you think could handle this situation and why?

What is Rescaling of Data and how is it done?