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2403 results · page 223/241

See reviews 1_MLP16 MLP Level 1
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  1. 1
    Explain your EDA.
    Times asked 8
  2. 2
    Pick any one EDA visualization and explain it in detail.
    Times asked 1
  3. 3
    Explain how any one model works.
    Times asked 1
  4. 4
    Why does Hyperparameter Tuning improve the model score?
    Times asked 1
  5. 5
    Explain any one Hyperparameter Tuning parameter.
    Times asked 1
See reviews Level3_29 MLP Level 1
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  1. 1
    Show your ID card.
    Times asked 23
  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
See reviews Level1_117 MLP Level 1
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  1. 1
    Show your ID card.
    Times asked 23
  2. 2
    Explain your notebook/project.
    Times asked 21
  3. 3
    Why did you apply a power transformation to the target variable?
    Times asked 1
  4. 4
    Why did you choose that specific power for the transformation?
    Times asked 1
  5. 5
    Explain XGBoost.
    Times asked 2
  6. 6
    Difference between XGBoost and Extra Trees Regressor.
    Times asked 1
See reviews Level1_14 MLP Level 1
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  1. 1
    Explain your notebook/project.
    Times asked 21
  2. 2
    Explain each section of your notebook.
    Times asked 1
  3. 3
    How did you use Pipeline?
    Times asked 1
  4. 4
    What is Cross Validation (CV)?
    Times asked 2
  5. 5
    How does Cross Validation work?
    Times asked 2
  6. 6
    What resources did you use?
    Times asked 2
  7. 7
    Which is your best model?
    Times asked 1
  8. 8
    Why is it your best model?
    Times asked 2
  9. 9
    What is Hyperparameter Tuning?
    Times asked 4
  10. 10
    Which Hyperparameter Tuning technique did you use?
    Times asked 1
  11. 11
    Explain Bagging.
    Times asked 6
  12. 12
    Explain Boosting.
    Times asked 6
  13. 13
    Explain the Hyperparameter Tuning section.
    Times asked 1
  14. 14
    What is the learning rate?
    Times asked 3
  15. 15
    Where and why is the learning rate used?
    Times asked 1
  16. 16
    Coding: Load the dataset.
    Times asked 1
  17. 17
    Coding: Filter rows where ManufactureYear > 2005.
    Times asked 1
  18. 18
    Coding: For the filtered dataset, find the distribution of the UtilizationTier column.
    Times asked 1
  19. 19
    Suggestion: Add insights for each model using graphs or tables.
    Times asked 1
  20. 20
    Suggestion: Revise MLT theory and practice live coding for Level 2.
    Times asked 1
See reviews Level3_31 MLP Level 1
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  1. 1
    Introduction.
    Times asked 9
  2. 2
    Tell me about yourself.
    Times asked 6
  3. 3
    Explain the problem statement.
    Times asked 6
  4. 4
    Explain your notebook/project.
    Times asked 21
  5. 5
    Explain why you performed each feature engineering step.
    Times asked 1
  6. 6
    Explain your EDA.
    Times asked 8
  7. 7
    Explain any graph you created.
    Times asked 1
  8. 8
    Which charts did you use and why?
    Times asked 1
  9. 9
    When should each type of chart be used?
    Times asked 1
  10. 10
    Explain your Pipeline.
    Times asked 1
  11. 11
    Explain your best model.
    Times asked 1
  12. 12
    Explain any model.
    Times asked 1
  13. 13
    How does the model work?
    Times asked 1
  14. 14
    What is Hyperparameter Tuning?
    Times asked 4
  15. 15
    Why is Hyperparameter Tuning required?
    Times asked 1
  16. 16
    Difference between GridSearchCV and RandomizedSearchCV.
    Times asked 5
  17. 17
    Explain the hyperparameters you tuned.
    Times asked 2
  18. 18
    What is the validation size in train_test_split?
    Times asked 1
  19. 19
    What is Mean, Median, and Mode?
    Times asked 1
  20. 20
    When should Mean, Median, and Mode be used for imputation?
    Times asked 1
  21. 21
    What is SimpleImputer?
    Times asked 2
  22. 22
    How do encoders work?
    Times asked 1
  23. 23
    Explain the encoding technique you used.
    Times asked 1
  24. 24
    What is Tokenization?
    Times asked 1
  25. 25
    Why do we remove stop words?
    Times asked 1
  26. 26
    Can custom stop words be used?
    Times asked 1
  27. 27
    What is a Word Cloud?
    Times asked 1
  28. 28
    Where is a Word Cloud useful?
    Times asked 1
  29. 29
    What is a Confusion Matrix?
    Times asked 2
  30. 30
    Explain F1-Score.
    Times asked 2
  31. 31
    What is the Loss Function?
    Times asked 2
  32. 32
    What is RMSE?
    Times asked 1
  33. 33
    Explain Bagging.
    Times asked 6
  34. 34
    Explain Boosting.
    Times asked 6
  35. 35
    Explain KNN.
    Times asked 3
  36. 36
    Explain K-Means.
    Times asked 2
  37. 37
    Difference between Supervised and Unsupervised Learning.
    Times asked 2
  38. 38
    What is overfitting?
    Times asked 5
  39. 39
    What is underfitting?
    Times asked 3
  40. 40
    Did you face overfitting or underfitting in your project?
    Times asked 1
  41. 41
    How did you handle it?
    Times asked 1
  42. 42
    What are outliers?
    Times asked 1
  43. 43
    How do you detect outliers?
    Times asked 1
  44. 44
    How do you handle outliers?
    Times asked 1
  45. 45
    Which visualization library did you use?
    Times asked 1
  46. 46
    Difference between Matplotlib and Seaborn.
    Times asked 1
  47. 47
    How many Kaggle submissions did you make?
    Times asked 1
  48. 48
    What changes improved your leaderboard score?
    Times asked 1
  49. 49
    How did you improve your project over different versions?
    Times asked 1
  50. 50
    Did you use AI tools during the project?
    Times asked 1
  51. 51
    How did you use AI?
    Times asked 1
  52. 52
    If you get stuck while building a model or debugging code, how would you solve it?
    Times asked 1
  53. 53
    Scenario: You are given a bank dataset to predict whether a customer will repay a credit bill. Explain your complete approach.
    Times asked 1
  54. 54
    Which model would you choose for that problem and why?
    Times asked 1
  55. 55
    Are you a student or a working professional?
    Times asked 1
  56. 56
    What are your future plans?
    Times asked 1
  57. 57
    Are you planning for higher studies?
    Times asked 1
  58. 58
    Are you participating in hackathons?
    Times asked 1
  59. 59
    Discuss your internships and projects.
    Times asked 1
See reviews Level2_124 MLP Level 1
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  1. 1
    Show your ID card.
    Times asked 23
  2. 2
    Introduction.
    Times asked 9
  3. 3
    Tell me about yourself.
    Times asked 6
  4. 4
    How was your experience with the project/course?
    Times asked 1
  5. 5
    Show your Kaggle leaderboard score and rank.
    Times asked 2
  6. 6
    Explain the problem statement.
    Times asked 6
  7. 7
    Explain your notebook/project.
    Times asked 21
  8. 8
    How can you improve your model accuracy further?
    Times asked 1
  9. 9
    How do you know your model is not overfitting or underfitting?
    Times asked 1
  10. 10
    How did you encode categorical features?
    Times asked 1
  11. 11
    Why did you use Label Encoding instead of One-Hot Encoding?
    Times asked 1
  12. 12
    What is data preprocessing?
    Times asked 2
  13. 13
    Why is preprocessing necessary?
    Times asked 1
  14. 14
    What is Hyperparameter Tuning?
    Times asked 4
  15. 15
    Why is Hyperparameter Tuning important?
    Times asked 1
  16. 16
    Difference between GridSearchCV and RandomizedSearchCV.
    Times asked 5
  17. 17
    Explain Logistic Regression.
    Times asked 5
  18. 18
    Why is Logistic Regression called "Regression" even though it is used for classification?
    Times asked 1
  19. 19
    What is the loss function of Logistic Regression?
    Times asked 3
  20. 20
    What does the C parameter in Logistic Regression represent?
    Times asked 2
  21. 21
    Explain Decision Tree.
    Times asked 3
  22. 22
    Where are Decision Trees used in real life?
    Times asked 1
  23. 23
    What is KNN?
    Times asked 1
  24. 24
    Why is KNN called a lazy learner?
    Times asked 2
  25. 25
    Difference between KNN and K-Means.
    Times asked 1
  26. 26
    Explain the K-Means algorithm.
    Times asked 1
  27. 27
    Difference between XGBoost and LightGBM.
    Times asked 5
  28. 28
    Why did you use CatBoost?
    Times asked 1
  29. 29
    Difference between Bagging and Boosting.
    Times asked 4
  30. 30
    Which should be preferred when the model is overfitting?
    Times asked 1
  31. 31
    Explain RFE.
    Times asked 1
  32. 32
    Explain evaluation metrics such as Accuracy, Precision, Recall, F1-Score, R² Score, and Confusion Matrix.
    Times asked 1
  33. 33
    How did you conclude that your model is not overfitting?
    Times asked 1
  34. 34
    What are the limitations of a Correlation Matrix?
    Times asked 1
  35. 35
    How can you detect non-linear relationships between features?
    Times asked 1
  36. 36
    Coding: Load a dataset.
    Times asked 1
  37. 37
    Coding: Show the first few rows of the dataset.
    Times asked 1
  38. 38
    Coding: Count the number of ? values in specified columns.
    Times asked 1
  39. 39
    Coding: Replace ? with NaN.
    Times asked 1
  40. 40
    Coding: Impute or remove missing values.
    Times asked 1
  41. 41
    Coding: Convert object/string columns to numeric.
    Times asked 1
  42. 42
    Coding: Filter rows based on given conditions.
    Times asked 1
  43. 43
    Coding: Find the correlation between two columns.
    Times asked 1
  44. 44
    Coding: Plot a correlation heatmap.
    Times asked 1
  45. 45
    Coding: Normalize selected columns using MinMaxScaler or StandardScaler.
    Times asked 1
  46. 46
    Coding: Perform RFE and select the top 2 features.
    Times asked 1
  47. 47
    Coding: Perform preprocessing before applying RFE.
    Times asked 1
  48. 48
    Coding: Add two columns (e.g., V1 + V2).
    Times asked 1
  49. 49
    Coding: Perform a train-test split on the Breast Cancer dataset.
    Times asked 1
  50. 50
    Coding: Load the Breast Cancer dataset and print its shape.
    Times asked 1
  51. 51
    Coding: Load the Iris dataset and perform RFE.
    Times asked 1
  52. 52
    Coding: Explain the RFE implementation after writing the code.
    Times asked 1
See reviews Level1_9 MLP Level 1
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  1. 1
    Introduction.
    Times asked 9
  2. 2
    Explain your data preprocessing.
    Times asked 1
  3. 3
    Explain the models you used.
    Times asked 1
  4. 4
    Why did your model perform well?
    Times asked 1
  5. 5
    What is Boosting?
    Times asked 2
  6. 6
    What are the important hyperparameters of Gradient Boosting Classifier (GBC)?
    Times asked 1
  7. 7
    What is Bagging?
    Times asked 2
  8. 8
    Explain One-Hot Encoding (OHE).
    Times asked 1
  9. 9
    Explain Ordinal Encoding.
    Times asked 2
  10. 10
    Why do we use sparse_output=False in OneHotEncoder?
    Times asked 1
  11. 11
    Explain your encoding.
    Times asked 2
  12. 12
    Explain your scaling.
    Times asked 2
  13. 13
    Explain your hyperparameters.
    Times asked 1
  14. 14
    Explain your feature engineering.
    Times asked 6
  15. 15
    Coding: Separate numerical and categorical columns.
    Times asked 1
  16. 16
    Coding: Create separate preprocessing pipelines for numerical and categorical columns.
    Times asked 1
  17. 17
    Show your application/project.
    Times asked 1
  18. 18
    Coding: Modify a query using a filter.
    Times asked 1
  19. 19
    What is the difference between Authentication and Authorization?
    Times asked 4
See reviews viva_leve1_viva2 MLP Level 1
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  1. 1
    Explain your notebook/project.
    Times asked 21
  2. 2
    Provide an in-depth explanation of your preprocessing.
    Times asked 1
  3. 3
    Provide an in-depth explanation of your feature engineering.
    Times asked 1
  4. 4
    Coding: Build the preprocessing Pipeline used in your notebook.
    Times asked 1
See reviews Level3_viva20 MLP Level 1
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  1. 1
    Explain notebook
    Times asked 1
  2. 2
    Explain how PCA works
    Times asked 1
  3. 3
    Did you do hyper parameter tuning
    Times asked 1
See reviews Viva18 MLP Level 1
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  1. 1
    Show your ID card.
    Times asked 23
  2. 2
    Explain your problem statement.
    Times asked 2
  3. 3
    Explain your notebook/project.
    Times asked 21
  4. 4
    How did you approach the problem?
    Times asked 2
  5. 5
    Explain your EDA.
    Times asked 8
  6. 6
    What did you show in the graphs?
    Times asked 1
  7. 7
    Explain the skewness of the target variable.
    Times asked 1
  8. 8
    Explain your preprocessing steps.
    Times asked 2
  9. 9
    Explain your feature engineering.
    Times asked 6
  10. 10
    Explain your model training.
    Times asked 1
  11. 11
    Which model did you use?
    Times asked 1
  12. 12
    How can you improve model performance?
    Times asked 1
  13. 13
    Explain your hyperparameter tuning.
    Times asked 3
  14. 14
    Show your hyperparameter tuning implementation.
    Times asked 1
  15. 15
    Explain your parameter grid.
    Times asked 1
  16. 16
    What do these hyperparameters mean? colsample_bytree learning_rate max_depth n_estimators subsample
    Times asked 1
  17. 17
    What happens if the learning rate is increased?
    Times asked 1
  18. 18
    What happens if the number of estimators is increased?
    Times asked 1
  19. 19
    What happens if the maximum depth is changed?
    Times asked 1
  20. 20
    Show your Pipeline.
    Times asked 1
  21. 21
    What is TSCV (Time Series Cross Validation)?
    Times asked 1
  22. 22
    Why did you use TSCV?
    Times asked 1
  23. 23
    What does the cv parameter mean in TSCV?
    Times asked 1
  24. 24
    Why can your dataset be considered time-series data?
    Times asked 1
  25. 25
    Why do we perform Cross Validation?
    Times asked 1
  26. 26
    Why did you use the rolling mean feature?
    Times asked 1
  27. 27
    What data/features did you not use, and why?
    Times asked 1
  28. 28
    What is the shape of the training dataset?
    Times asked 1
  29. 29
    What is the shape of the test dataset?
    Times asked 1
  30. 30
    Print the first five rows of the training dataset.
    Times asked 1
  31. 31
    Show the basic statistics of the training dataset using describe().
    Times asked 1
  32. 32
    Count the null values in the training dataset.
    Times asked 1
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