PacktPub - Data Cleansing Master Class in Python

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Files
  1. Exercises Files.zip 304.42KB
  2. Section 1/01.01-course_introduction.mkv 152.85MB
  3. Section 1/01.02-course_structure.mkv 157.17MB
  4. Section 1/01.03-is_this_course_right_for_you.mkv 4.25MB
  5. Section 2/02.01-introducing_data_preparation.mkv 277.14MB
  6. Section 2/02.02-the_machine_learning_process.mkv 90.77MB
  7. Section 2/02.03-data_preparation_defined.mkv 251.94MB
  8. Section 2/02.04-choosing_a_data_preparation_technique.mkv 264.02MB
  9. Section 2/02.05-what_is_data_in_machine_learning.mkv 75.71MB
  10. Section 2/02.06-raw_data.mkv 115.35MB
  11. Section 2/02.07-machine_learning_is_mostly_data_preparation.mkv 29.11MB
  12. Section 2/02.08-common_data_preparation_tasks-data_cleansing.mkv 160.19MB
  13. Section 2/02.09-common_data_preparation_tasks-feature_selection.mkv 51.66MB
  14. Section 2/02.10-common_data_preparation_tasks-data_transforms.mkv 10.46MB
  15. Section 2/02.11-common_data_preparation_tasks-feature_engineering.mkv 134.62MB
  16. Section 2/02.12-common_data_preparation_tasks-dimensionality_reduction.mkv 9.14MB
  17. Section 2/02.13-data_leakage.mkv 11.27MB
  18. Section 2/02.14-problem_with_naive_data_preparation.mkv 142.92MB
  19. Section 2/02.15-case_study_data_leakage_train__test__split_naive_approach.mkv 46.89MB
  20. Section 2/02.16-case_study_data_leakage_train__test__split_correct_approach.mkv 27.23MB
  21. Section 2/02.17-case_study_data_leakage_k-fold_naive_approach.mkv 39.56MB
  22. Section 2/02.18-case_study_data_leakage_k-fold_correct_approach.mkv 35.40MB
  23. Section 3/03.01-data_cleansing_overview.mkv 159.66MB
  24. Section 3/03.02-identify_columns_that_contain_a_single_value.mkv 18.12MB
  25. Section 3/03.03-identify_columns_with_few_values.mkv 31.19MB
  26. Section 3/03.04-remove_columns_with_low_variance.mkv 29.14MB
  27. Section 3/03.05-identify_and_remove_rows_that_contain_duplicate_data.mkv 110.79MB
  28. Section 3/03.06-defining_outliers.mkv 97.67MB
  29. Section 3/03.07-remove_outliers-the_standard_deviation_approach.mkv 50.00MB
  30. Section 3/03.08-remove_outliers-the_iqr_approach.mkv 40.68MB
  31. Section 3/03.09-automatic_outlier_detection.mkv 50.22MB
  32. Section 3/03.10-mark_missing_values.mkv 60.03MB
  33. Section 3/03.11-remove_rows_with_missing_values.mkv 27.74MB
  34. Section 3/03.12-statistical_imputation.mkv 5.98MB
  35. Section 3/03.13-mean_value_imputation.mkv 41.86MB
  36. Section 3/03.14-simple_imputer_with_model_evaluation.mkv 21.26MB
  37. Section 3/03.15-compare_different_statistical_imputation_strategies.mkv 25.32MB
  38. Section 3/03.16-k-nearest_neighbors_imputation.mkv 44.39MB
  39. Section 3/03.17-knnimputer_and_model_evaluation.mkv 34.33MB
  40. Section 3/03.18-iterative_imputation.mkv 37.61MB
  41. Section 3/03.19-iterativeimputer_and_model_evaluation.mkv 18.41MB
  42. Section 3/03.20-iterativeimputer_and_different_imputation_order.mkv 23.03MB
  43. Section 4/04.01-feature_selection_introduction.mkv 203.11MB
  44. Section 4/04.02-feature_selection_defined.mkv 11.88MB
  45. Section 4/04.03-statistics_for_feature_selection.mkv 104.30MB
  46. Section 4/04.04-loading_a_categorical_dataset.mkv 27.65MB
  47. Section 4/04.05-encode_the_dataset_for_modelling.mkv 25.02MB
  48. Section 4/04.06-chi-squared.mkv 17.49MB
  49. Section 4/04.07-mutual_information.mkv 18.20MB
  50. Section 4/04.08-modeling_with_selected_categorical_features.mkv 37.43MB
  51. Section 4/04.09-feature_selection_with_anova_on_numerical_input.mkv 41.78MB
  52. Section 4/04.10-feature_selection_with_mutual_information.mkv 18.20MB
  53. Section 4/04.11-modeling_with_selected_numerical_features.mkv 25.98MB
  54. Section 4/04.12-tuning_a_number_of_selected_features.mkv 37.97MB
  55. Section 4/04.13-select_features_for_numerical_output.mkv 22.68MB
  56. Section 4/04.14-linear_correlation_with_correlation_statistics.mkv 26.18MB
  57. Section 4/04.15-linear_correlation_with_mutual_information.mkv 29.38MB
  58. Section 4/04.16-baseline_and_model_built_using_correlation.mkv 35.73MB
  59. Section 4/04.17-model_built_using_mutual_information_features.mkv 11.42MB
  60. Section 4/04.18-tuning_number_of_selected_features.mkv 54.69MB
  61. Section 4/04.19-recursive_feature_elimination.mkv 176.57MB
  62. Section 4/04.20-rfe_for_classification.mkv 51.03MB
  63. Section 4/04.21-rfe_for_regression.mkv 26.21MB
  64. Section 4/04.22-rfe_hyperparameters.mkv 32.64MB
  65. Section 4/04.23-feature_ranking_for_rfe.mkv 29.59MB
  66. Section 4/04.24-feature_importance_scores_defined.mkv 187.17MB
  67. Section 4/04.25-feature_importance_scores_linear_regression.mkv 35.15MB
  68. Section 4/04.26-feature_importance_scores_logistic_regression_and_cart.mkv 36.53MB
  69. Section 4/04.27-feature_importance_scores_random_forests.mkv 17.01MB
  70. Section 4/04.28-permutation_feature_importance.mkv 28.41MB
  71. Section 4/04.29-feature_selection_with_importance.mkv 42.35MB
  72. Section 5/05.01-scale_numerical_data.mkv 11.06MB
  73. Section 5/05.02-diabetes_dataset_for_scaling.mkv 23.04MB
  74. Section 5/05.03-minmaxscaler_transform.mkv 24.25MB
  75. Section 5/05.04-standardscaler_transform.mkv 28.50MB
  76. Section 5/05.05-robust_scaling_data.mkv 42.49MB
  77. Section 5/05.06-robust_scaler_applied_to_dataset.mkv 22.60MB
  78. Section 5/05.07-explore_robust_scaler_range.mkv 14.91MB
  79. Section 5/05.08-nominal_and_ordinal_variables.mkv 301.65MB
  80. Section 5/05.09-ordinal_encoding.mkv 17.01MB
  81. Section 5/05.10-one-hot_encoding_defined.mkv 3.70MB
  82. Section 5/05.11-one-hot_encoding.mkv 17.27MB
  83. Section 5/05.12-dummy_variable_encoding.mkv 17.46MB
  84. Section 5/05.13-ordinal_encoder_transform_on_breast_cancer_dataset.mkv 45.66MB
  85. Section 5/05.14-make_distributions_more_gaussian.mkv 8.88MB
  86. Section 5/05.15-power_transform_on_contrived_dataset.mkv 21.34MB
  87. Section 5/05.16-power_transform_on_sonar_dataset.mkv 28.99MB
  88. Section 5/05.17-box-cox_on_sonar_dataset.mkv 31.78MB
  89. Section 5/05.18-yeo-johnson_on_sonar_dataset.mkv 26.03MB
  90. Section 5/05.19-polynomial_features.mkv 152.87MB
  91. Section 5/05.20-effect_of_polynomial_degrees.mkv 19.25MB
  92. Section 6/06.01-transforming_different_data_types.mkv 23.42MB
  93. Section 6/06.02-the_columntransformer.mkv 28.22MB
  94. Section 6/06.03-the_columntransformer_on_abalone_dataset.mkv 35.33MB
  95. Section 6/06.04-manually_transform_target_variable.mkv 24.53MB
  96. Section 6/06.05-automatically_transform_target_variable.mkv 54.43MB
  97. Section 6/06.06-challenge_of_preparing_new_data_for_a_model.mkv 246.91MB
  98. Section 6/06.07-save_model_and_data_scaler.mkv 40.38MB
  99. Section 6/06.08-load_and_apply_saved_scalers.mkv 17.94MB
  100. Section 7/07.01-curse_of_dimensionality.mkv 14.33MB
  101. Section 7/07.02-techniques_for_dimensionality_reduction.mkv 97.49MB
  102. Section 7/07.03-linear_discriminant_analysis.mkv 19.26MB
  103. Section 7/07.04-linear_discriminant_analysis_demonstrated.mkv 49.11MB
  104. Section 7/07.05-principal_component_analysis.mkv 59.75MB