PacktPub - Data Cleansing Master Class in Python

1. Section 1/01.01-course_introduction.mkv152.85MB
2. Section 1/01.02-course_structure.mkv157.17MB
3. Section 1/01.03-is_this_course_right_for_you.mkv4.25MB
4. Section 2/02.01-introducing_data_preparation.mkv277.14MB
5. Section 2/02.02-the_machine_learning_process.mkv90.77MB
6. Section 2/02.03-data_preparation_defined.mkv251.94MB
7. Section 2/02.04-choosing_a_data_preparation_technique.mkv264.02MB
8. Section 2/02.05-what_is_data_in_machine_learning.mkv75.71MB
9. Section 2/02.06-raw_data.mkv115.35MB
10. Section 2/02.07-machine_learning_is_mostly_data_preparation.mkv29.11MB
11. Section 2/02.08-common_data_preparation_tasks-data_cleansing.mkv160.19MB
12. Section 2/02.09-common_data_preparation_tasks-feature_selection.mkv51.66MB
13. Section 2/02.10-common_data_preparation_tasks-data_transforms.mkv10.46MB
14. Section 2/02.11-common_data_preparation_tasks-feature_engineering.mkv134.62MB
15. Section 2/02.12-common_data_preparation_tasks-dimensionality_reduction.mkv9.14MB
16. Section 2/02.13-data_leakage.mkv11.27MB
17. Section 2/02.14-problem_with_naive_data_preparation.mkv142.92MB
18. Section 2/02.15-case_study_data_leakage_train__test__split_naive_approach.mkv46.89MB
19. Section 2/02.16-case_study_data_leakage_train__test__split_correct_approach.mkv27.23MB
20. Section 2/02.17-case_study_data_leakage_k-fold_naive_approach.mkv39.56MB
21. Section 2/02.18-case_study_data_leakage_k-fold_correct_approach.mkv35.4MB
22. Section 3/03.01-data_cleansing_overview.mkv159.66MB
23. Section 3/03.02-identify_columns_that_contain_a_single_value.mkv18.12MB
24. Section 3/03.03-identify_columns_with_few_values.mkv31.19MB
25. Section 3/03.04-remove_columns_with_low_variance.mkv29.14MB
26. Section 3/03.05-identify_and_remove_rows_that_contain_duplicate_data.mkv110.79MB
27. Section 3/03.06-defining_outliers.mkv97.67MB
28. Section 3/03.07-remove_outliers-the_standard_deviation_approach.mkv50MB
29. Section 3/03.08-remove_outliers-the_iqr_approach.mkv40.68MB
30. Section 3/03.09-automatic_outlier_detection.mkv50.22MB
31. Section 3/03.10-mark_missing_values.mkv60.03MB
32. Section 3/03.11-remove_rows_with_missing_values.mkv27.74MB
33. Section 3/03.12-statistical_imputation.mkv5.98MB
34. Section 3/03.13-mean_value_imputation.mkv41.86MB
35. Section 3/03.14-simple_imputer_with_model_evaluation.mkv21.26MB
36. Section 3/03.15-compare_different_statistical_imputation_strategies.mkv25.32MB
37. Section 3/03.16-k-nearest_neighbors_imputation.mkv44.39MB
38. Section 3/03.17-knnimputer_and_model_evaluation.mkv34.33MB
39. Section 3/03.18-iterative_imputation.mkv37.61MB
40. Section 3/03.19-iterativeimputer_and_model_evaluation.mkv18.41MB
41. Section 3/03.20-iterativeimputer_and_different_imputation_order.mkv23.03MB
42. Section 4/04.01-feature_selection_introduction.mkv203.11MB
43. Section 4/04.02-feature_selection_defined.mkv11.88MB
44. Section 4/04.03-statistics_for_feature_selection.mkv104.3MB
45. Section 4/04.04-loading_a_categorical_dataset.mkv27.65MB
46. Section 4/04.05-encode_the_dataset_for_modelling.mkv25.02MB
47. Section 4/04.06-chi-squared.mkv17.49MB
48. Section 4/04.07-mutual_information.mkv18.2MB
49. Section 4/04.08-modeling_with_selected_categorical_features.mkv37.43MB
50. Section 4/04.09-feature_selection_with_anova_on_numerical_input.mkv41.78MB
51. Section 4/04.10-feature_selection_with_mutual_information.mkv18.2MB
52. Section 4/04.11-modeling_with_selected_numerical_features.mkv25.98MB
53. Section 4/04.12-tuning_a_number_of_selected_features.mkv37.97MB
54. Section 4/04.13-select_features_for_numerical_output.mkv22.68MB
55. Section 4/04.14-linear_correlation_with_correlation_statistics.mkv26.18MB
56. Section 4/04.15-linear_correlation_with_mutual_information.mkv29.38MB
57. Section 4/04.16-baseline_and_model_built_using_correlation.mkv35.73MB
58. Section 4/04.17-model_built_using_mutual_information_features.mkv11.42MB
59. Section 4/04.18-tuning_number_of_selected_features.mkv54.69MB
60. Section 4/04.19-recursive_feature_elimination.mkv176.57MB
61. Section 4/04.20-rfe_for_classification.mkv51.03MB
62. Section 4/04.21-rfe_for_regression.mkv26.21MB
63. Section 4/04.22-rfe_hyperparameters.mkv32.64MB
64. Section 4/04.23-feature_ranking_for_rfe.mkv29.59MB
65. Section 4/04.24-feature_importance_scores_defined.mkv187.17MB
66. Section 4/04.25-feature_importance_scores_linear_regression.mkv35.15MB
67. Section 4/04.26-feature_importance_scores_logistic_regression_and_cart.mkv36.53MB
68. Section 4/04.27-feature_importance_scores_random_forests.mkv17.01MB
69. Section 4/04.28-permutation_feature_importance.mkv28.41MB
70. Section 4/04.29-feature_selection_with_importance.mkv42.35MB
71. Section 5/05.01-scale_numerical_data.mkv11.06MB
72. Section 5/05.02-diabetes_dataset_for_scaling.mkv23.04MB
73. Section 5/05.03-minmaxscaler_transform.mkv24.25MB
74. Section 5/05.04-standardscaler_transform.mkv28.5MB
75. Section 5/05.05-robust_scaling_data.mkv42.49MB
76. Section 5/05.06-robust_scaler_applied_to_dataset.mkv22.6MB
77. Section 5/05.07-explore_robust_scaler_range.mkv14.91MB
78. Section 5/05.08-nominal_and_ordinal_variables.mkv301.65MB
79. Section 5/05.09-ordinal_encoding.mkv17.01MB
80. Section 5/05.10-one-hot_encoding_defined.mkv3.7MB
81. Section 5/05.11-one-hot_encoding.mkv17.27MB
82. Section 5/05.12-dummy_variable_encoding.mkv17.46MB
83. Section 5/05.13-ordinal_encoder_transform_on_breast_cancer_dataset.mkv45.66MB
84. Section 5/05.14-make_distributions_more_gaussian.mkv8.88MB
85. Section 5/05.15-power_transform_on_contrived_dataset.mkv21.34MB
86. Section 5/05.16-power_transform_on_sonar_dataset.mkv28.99MB
87. Section 5/05.17-box-cox_on_sonar_dataset.mkv31.78MB
88. Section 5/05.18-yeo-johnson_on_sonar_dataset.mkv26.03MB
89. Section 5/05.19-polynomial_features.mkv152.87MB
90. Section 5/05.20-effect_of_polynomial_degrees.mkv19.25MB
91. Section 6/06.01-transforming_different_data_types.mkv23.42MB
92. Section 6/06.02-the_columntransformer.mkv28.22MB
93. Section 6/06.03-the_columntransformer_on_abalone_dataset.mkv35.33MB
94. Section 6/06.04-manually_transform_target_variable.mkv24.53MB
95. Section 6/06.05-automatically_transform_target_variable.mkv54.43MB
96. Section 6/06.06-challenge_of_preparing_new_data_for_a_model.mkv246.91MB
97. Section 6/06.07-save_model_and_data_scaler.mkv40.38MB
98. Section 6/06.08-load_and_apply_saved_scalers.mkv17.94MB
99. Section 7/07.01-curse_of_dimensionality.mkv14.33MB
100. Section 7/07.02-techniques_for_dimensionality_reduction.mkv97.49MB
101. Section 7/07.03-linear_discriminant_analysis.mkv19.26MB
102. Section 7/07.04-linear_discriminant_analysis_demonstrated.mkv49.11MB
103. Section 7/07.05-principal_component_analysis.mkv59.75MB