Viva prep · Real questions · Student experiences Enroll in Bootcamp

Proctor workspace

Proctor Level3_29

Share your experience Add your viva experience here
114 Questions
1 Sets
0 Topics
0 Reviews

Student reviews

No student reviews for this proctor yet.

Approved viva sets

See reviews Level3_29 MLP Level 1
Copy set link
  1. 1
    Show your ID card.
    Times asked 22
  2. 2
    What did you learn from this project/course?
    Times asked 1
  3. 3
    What is Machine Learning?
    Times asked 2
  4. 4
    Difference between Machine Learning and Data Science.
    Times asked 1
  5. 5
    Difference between Machine Learning and Deep Learning.
    Times asked 1
  6. 6
    What are the types of Machine Learning?
    Times asked 2
  7. 7
    Explain Supervised Learning.
    Times asked 1
  8. 8
    Explain Unsupervised Learning.
    Times asked 1
  9. 9
    Explain Semi-Supervised Learning.
    Times asked 1
  10. 10
    Explain Reinforcement Learning.
    Times asked 1
  11. 11
    Have you implemented Reinforcement Learning anywhere?
    Times asked 1
  12. 12
    Name three Unsupervised Learning algorithms.
    Times asked 1
  13. 13
    Explain your problem statement.
    Times asked 2
  14. 14
    Explain your notebook/project.
    Times asked 21
  15. 15
    How did you approach the problem?
    Times asked 2
  16. 16
    What preprocessing steps did you perform?
    Times asked 2
  17. 17
    How did you handle missing values?
    Times asked 4
  18. 18
    Did you detect outliers?
    Times asked 1
  19. 19
    How did you handle outliers?
    Times asked 3
  20. 20
    What other outlier detection methods do you know besides IQR?
    Times asked 1
  21. 21
    Explain your feature engineering.
    Times asked 6
  22. 22
    Explain your Pipeline and ColumnTransformer.
    Times asked 1
  23. 23
    Explain the charts you created.
    Times asked 1
  24. 24
    What insights would you give to the business from your analysis?
    Times asked 1
  25. 25
    What is Correlation?
    Times asked 2
  26. 26
    Which correlation method did you use?
    Times asked 2
  27. 27
    Why did you choose Pearson Correlation?
    Times asked 1
  28. 28
    Why did you select only a few features for the Correlation Matrix?
    Times asked 1
  29. 29
    Explain LightGBM.
    Times asked 2
  30. 30
    What does "Light" mean in LightGBM?
    Times asked 1
  31. 31
    Difference between XGBoost and LightGBM.
    Times asked 5
  32. 32
    Explain Random Forest.
    Times asked 2
  33. 33
    Why did you use Random Forest?
    Times asked 2
  34. 34
    What does "Forest" mean in Random Forest?
    Times asked 1
  35. 35
    Difference between Random Forest and XGBoost.
    Times asked 2
  36. 36
    What is n_estimators?
    Times asked 2
  37. 37
    How does the learning rate affect training?
    Times asked 1
  38. 38
    What happens if the learning rate changes from 0.1 to 0.01?
    Times asked 1
  39. 39
    Explain SVM.
    Times asked 3
  40. 40
    How does SVM work?
    Times asked 1
  41. 41
    Explain KNN.
    Times asked 3
  42. 42
    Explain K-Means.
    Times asked 2
  43. 43
    How does K-Means work?
    Times asked 1
  44. 44
    Difference between K-Means and K-Means++.
    Times asked 2
  45. 45
    What are the steps of the K-Means algorithm?
    Times asked 1
  46. 46
    What is PCA?
    Times asked 3
  47. 47
    How does PCA work?
    Times asked 3
  48. 48
    Is PCA supervised or unsupervised?
    Times asked 1
  49. 49
    How does PCA reduce features?
    Times asked 1
  50. 50
    How does PCA affect model performance?
    Times asked 1
  51. 51
    What is the optimal number of principal components?
    Times asked 1
  52. 52
    What are tree-based algorithms?
    Times asked 1
  53. 53
    How is the root node selected in a Decision Tree?
    Times asked 1
  54. 54
    What is Entropy?
    Times asked 2
  55. 55
    What is Information Gain?
    Times asked 3
  56. 56
    Relationship between Entropy and Information Gain.
    Times asked 1
  57. 57
    What is Gini Impurity?
    Times asked 1
  58. 58
    What is a pure node?
    Times asked 1
  59. 59
    What is an impure node?
    Times asked 1
  60. 60
    What is the height of a Decision Tree?
    Times asked 1
  61. 61
    What is the depth of a Decision Tree?
    Times asked 1
  62. 62
    Difference between a Tree and a Graph.
    Times asked 1
  63. 63
    What is Overfitting?
    Times asked 1
  64. 64
    How do you prevent Overfitting?
    Times asked 1
  65. 65
    What is Regularization?
    Times asked 1
  66. 66
    How does Regularization work?
    Times asked 1
  67. 67
    Explain L1 and L2 Regularization.
    Times asked 1
  68. 68
    What is Gradient Descent?
    Times asked 2
  69. 69
    Difference between Batch Gradient Descent and Stochastic Gradient Descent.
    Times asked 1
  70. 70
    What is the Loss Function?
    Times asked 2
  71. 71
    Loss function of Logistic Regression.
    Times asked 2
  72. 72
    Loss function of Random Forest.
    Times asked 1
  73. 73
    Loss function of Ridge Regression.
    Times asked 1
  74. 74
    Why is Mean Squared Error (MSE) used?
    Times asked 1
  75. 75
    What is R² Score?
    Times asked 3
  76. 76
    What is Precision?
    Times asked 2
  77. 77
    What is Recall?
    Times asked 2
  78. 78
    What is F1-Score?
    Times asked 3
  79. 79
    What is F1 Macro?
    Times asked 1
  80. 80
    Write the formula for F1-Score.
    Times asked 2
  81. 81
    What is a Confusion Matrix?
    Times asked 2
  82. 82
    What is TF-IDF?
    Times asked 2
  83. 83
    Write the TF-IDF formula.
    Times asked 1
  84. 84
    What are Activation Functions?
    Times asked 1
  85. 85
    Name different Activation Functions.
    Times asked 1
  86. 86
    What is ReLU?
    Times asked 1
  87. 87
    What is the input and output range of ReLU?
    Times asked 1
  88. 88
    How many hidden layers can a Neural Network have?
    Times asked 1
  89. 89
    What happens if the number of hidden layers increases?
    Times asked 1
  90. 90
    What are optimization functions in Neural Networks?
    Times asked 1
  91. 91
    What are different types of Encoders?
    Times asked 1
  92. 92
    Why is StandardScaler used?
    Times asked 1
  93. 93
    What is n_jobs?
    Times asked 1
  94. 94
    Coding: Create a dataset using random numbers.
    Times asked 1
  95. 95
    Coding: Create a random dataset with specified dimensions.
    Times asked 1
  96. 96
    Coding: Load the Digits dataset.
    Times asked 1
  97. 97
    Coding: Print the size/dimensions of the Digits dataset.
    Times asked 1
  98. 98
    Coding: Print the output classes of the Digits dataset.
    Times asked 1
  99. 99
    Coding: Apply K-Means and find the optimal number of clusters.
    Times asked 1
  100. 100
    Coding: Plot the Elbow Curve.
    Times asked 1
  101. 101
    Coding: Apply SVM on the Digits dataset.
    Times asked 1
  102. 102
    Coding: Apply a Decision Tree on the Iris dataset.
    Times asked 1
  103. 103
    Coding: Plot the Decision Tree.
    Times asked 1
  104. 104
    Coding: Load the Wine dataset.
    Times asked 1
  105. 105
    Coding: Split the dataset into training and testing sets.
    Times asked 1
  106. 106
    Coding: Train a Random Forest model.
    Times asked 1
  107. 107
    Coding: Find the model accuracy.
    Times asked 1
  108. 108
    Coding: Apply PCA before training.
    Times asked 1
  109. 109
    Coding: Compare accuracy and training time before and after PCA.
    Times asked 1
  110. 110
    Coding: Plot Accuracy vs. Number of Principal Components.
    Times asked 1
  111. 111
    Coding: Explain the PCA performance graph.
    Times asked 1
  112. 112
    Coding: Load a news article and generate the TF-IDF matrix.
    Times asked 1
  113. 113
    Coding: Stack a dataset using hstack.
    Times asked 1
  114. 114
    Coding: Use mutual_info_regression to find the top 5 features.
    Times asked 1
Tag summary

No tags.

Created for educational purposes only. Questions are based on students' personal experiences and may not reflect actual exam content.