Title: Special Edition Data Science Interview Questions Solved in Python and Spark : With Deep Learning and Reinforcement Learning Bonus Topics in Keras
Publisher: CreateSpace Independent Publishing Platform
Publication Date: 2016
Language: English
ISBN 10: 1534795715
ISBN 13: 9781534795716
Binding: Soft cover
Condition: As New

About this title

Synopsis

Special Edition Data Science and Machine Learning Interview Questions Solved in Python and Spark with Deep Learning and Reinforcement Learning Bonus Questions

From the Back Cover

Introduction 17

1. What are the most important machine learning techniques? 17

Solution 17

2. Why is it important to have a robust set of metrics for machine learning? 18

Solution 18

Code 19

Feature Engineering and the ETL Process (Extraction, Transformation, Loading) 20

3. Why are Features extraction and engineering so important in machine learning? 20

Solution 20

4. Can you provide an example of features extraction? 22

Solution 22

Code 22

5. What is the mean, the variance, and the covariance? 23

Solution 23

Code 23

6. What are percentiles and quartiles? 24

Solution 24

Code 24

7. Why are vectors and norms used in machine learning? 24

Solution 24

Code 25

8. What is a convenient tool for performing data statistics? 25

Solution 25

Code 25

9. How is it convenient to visualize data statistics 26

Solution 26

Code 26

10. How to compute covariance and correlation matrices with pandas 27

Solution 27

Code 27

11. What is a TFxIDF? 28

Solution 28

Code 29

12. What is "features hashing"? And why is it useful for BigData? 29

Solution 29

13. What is "continuous features binning"? 30

Solution 30

14. What is an LP normalization? 30

Solution 30

Code 30

15. What is a Chi Square Selection? 31

Solution 31

16. What is mutual information and how can it be used for features selection? 31

Solution 31

17. How to deal with categorical features? And what is one-hot-encoding? 32

Solution 32

Code 32

18. Can you transform an XML file into Python Pandas? 33

Solution 33

Code 34

19. Can you read HTML into Python Pandas? 35

Solution 35

Code 35

20. Can you read JSON into Python Pandas? 35

Solution 35

Code 35

21. Can you draw a function from Python? 36

Solution 36

Code 36

22. What is a Gaussian? 37

Solution 37

Code 37

23. What is a Standard Scaling? 38

Solution 38

Code 38

24. Why are statistical distributions important? 39

Solution 39

Code 41

25. Can you compare your data with some distribution? What is a qq-plot? 41

Solution 41

Code 41

26. Can you provide an example of connection to the Twitter API? 42

Solution 42

Code 42

27. Can you provide an example of connection to the LinkedIn API? 44

Solution 44

Code 44

28. Can you provide an example of connection to the Facebook API? 44

Solution 44

Code 45

29. What is Parquet? 45

Solution 45

Code 45

Machine learning basics 46

30. What is a Bias - Variance tradeoff? 46

Solution 46

31. What is a training set, a validation set, a test set and a gold set in supervised and unsupervised learning? 47

Solution 47

32. What is a cross-validation and what is an overfitting? 48

Solution 48

Code 49

33. Why is Grid Search important? 50

Solution 50

Code 50

Spark and python 52

34. What is an Ipython notebook? 52

Solution 52

Code 52

35. What are Numpy, Scipy and Spark essential datatypes? 53

Solution 53

Code 54

36. Can you provide an example for Map and Reduce in Spark? (Let's compute the Mean Square Error) 54

Solution 54

Code 55

37. Can you provide examples for other computations in Spark? 56

Solution 56

Code 60

38. How does Python interact with Spark 60

Solution 60

39. What is Spark support for Machine Learning? 61

Solution 61

40. How does Spark work in a parallel environment 61

Solution 61

Code 61

41. What are the new Spark DataFrame and the Spark Pipeline? And how we can use the new ML library for Grid Search 62

Solution 62

Code 63

Linear Models and Regression 66

42. What is a loss function, what are linear models, and what do we mean by regularization parameters in machine learning? 66

Solution 66

43. What is an odd ratio? 68

Solution 68

44. What is a sigmoid function and what is a logistic function? 69

Solution 69

Code 70

45. What is a Linear Least Square Regression? 70

Solution 70

Code 71

46. What are Lasso, Ridge, and ElasticNet regularizations? 72

Solution 72

47. What is a Logistic Regression? 72

Solution 72

Code 73

48. What is a stepwise regression? 74

Solution 74

49. What is an isotonic regression? 75

Solution 75

Code 75

50. How to include nonlinear information into linear models 76

Solution 76

51. What are generalized linear models and what is an R Formula? 77

Solution 77

Code 77

52. What is LARS? 78

Solution 78

53. What is GMLNET? 79

Solution 79

Optimization techniques 81

54. What is a gradient descent? 81

Solution 81

55. What is a stochastic gradient descent? 82

Solution 82

Code 83

56. What is momentum? 83

Solution 83

57. What is Conjugate Gradient? 84

Solution 84

58. What are Adagrad, RSMProp, Adam, and L-BFGS? 85

Classification 86

59. What is a Na�ve Bayes classifier? 86

Solution 86

60. What is a Bernoulli and a Multivariate Na�ve Bayes? 88

Solution 88

Code 89

61. What is a Gaussian Na�ve Bayes? 90

Solution 90

62. What is another way to use Na�ve Bayes with continuous data? 90

Solution 90

63. What is the Nearest Neighbor classification? 90

Solution 90

Code 92

64. What are Support Vector Machines (SVM)? 92

Solution 92

Code 94

65. What are SVM Kernel tricks? 95

Solution 95

67. What is SVM with soft margins? 96

Solution 96

66. Can you provide an example for Text Classification with Spark? 96

Solution 96

Code 97

Clustering 98

67. What is K-Means Clustering? 98

Solution 98

Code 99

68. What is the DBSCAN clustering algorithm? 99

Solution 99

Code 100

69. What is a Streaming K-Means? 101

Solution 101

Code 101

70. What is Canopi Clusterting? 102

Solution 102

71. What is Bisecting K-Means? 103

Solution 103

72. What is the Expectation Maximization Clustering algorithm? 103

Solution 103

73. What is a Gaussian Mixture? 105

Solution 105

Code 105

Boosting and Ensembles 107

74. What are the Ensembles? 107

Solution 107

75. What is an AdaBoost classification algorithm? 107

Solution 107

Decision Trees, Gradient Boosted Trees and Random Forests 109

76. What are the Decision Trees? 109

Solution 109

Code 111

77. What is a Gradient Boosted Tree? 112

Solution 112

78. What is a Gradient Boosted Trees Regressor? 112

Solution 112

Code 112

79. Gradient Boosted Trees Classification 114

Solution 114

Code 114

80. What is a Random Forest? 115

Solution 115

Code 115

Recommendations 117

81. What is a recommender system? 117

Solution 117

82. What is a collaborative filtering ALS algorithm? 118

Solution 118

Code 119

Dimensional Reduction 121

83. What is the PCA Dimensional reduction technique? 121

Solution 121

Code 122

84. What is the SVD Dimensional reduction technique? 123

Solution 123

Code 123

85. What is a Latent Semantic Analysis (LSA)? 124

Solution 124

86. What is the Latent Dirichlet Allocation topic model? 124

Solution 124

Code 125

Associative Rules 127

87. What is the Associative Rule Learning? 127

Solution 127

88. What is FP-growth? 129

Solution 129

Code 129

Graph Mining 130

89. Can you represent a graph in Python? 130

Solution 130

Code 130

90. How to use the GraphX Library 130

Solution 130

91. What is PageRank? And how to compute it with GraphX 131

Solution 131

Code 132

92. What is a Power Iteration Clustering? 134

Solution 134

Code 134

Neural Networks 135

93. What is a Perceptron? 135

Solution 135

94. What is an ANN (Artificial Neural Network)? 136

Solution 136

95. What are the activation functions? 137

Solution 137

96. How many types of Neural Networks are known? 138

97. How to train a Neural Network 139

Solution 139

98. Which are the possible ANNs applications? 139

Solution

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