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  • U: Anonymous
  • D: 2022-03-01 01:58:35
  • C: Unknown

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ReScene version pyReScene Auto 0.7 XQZT File size CRC
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46,560
Stored files
530 52417C44
28,033 D4DF36EC
693 1936FD87
RAR-files
packt.data.cleansing.master.class.in.python-xqzt.rar 600,000,000 F9925D32
packt.data.cleansing.master.class.in.python-xqzt.r00 600,000,000 FD7A838D
packt.data.cleansing.master.class.in.python-xqzt.r01 600,000,000 AEF49E1C
packt.data.cleansing.master.class.in.python-xqzt.r02 600,000,000 8BBA8C33
packt.data.cleansing.master.class.in.python-xqzt.r03 600,000,000 48111742
packt.data.cleansing.master.class.in.python-xqzt.r04 600,000,000 8A97CC41
packt.data.cleansing.master.class.in.python-xqzt.r05 600,000,000 CB5D8F72
packt.data.cleansing.master.class.in.python-xqzt.r06 600,000,000 6B64AAC7
packt.data.cleansing.master.class.in.python-xqzt.r07 600,000,000 76082DB3
packt.data.cleansing.master.class.in.python-xqzt.r08 600,000,000 7ECBCBCE
packt.data.cleansing.master.class.in.python-xqzt.r09 288,764,811 77A8F7FF
Total size: 6,288,764,811
Archived files
01.01-course_introduction.mkv [d0fa8720bc9bed2] 160,278,554 BCB376CA
01.02-course_structure.mkv [d02791386bacc4b3] 164,801,946 33A313AA
01.03-is_this_course_right_for_you.mkv [be89bfa22d66010c] 4,460,070 A8E534BC
02.01-introducing_data_preparation.mkv [2e20ec753d864f02] 290,606,714 967BF9A8
02.02-the_machine_learning_process.mkv [d426526d240ebef1] 95,182,497 E4CF1ED9
02.03-data_preparation_defined.mkv [c6f6f73a11f5251c] 264,173,819 5C0A5210
02.04-choosing_a_data_preparation_technique.mkv [9ac464011ed2d0b0] 276,846,412 A12D7759
02.05-what_is_data_in_machine_learning.mkv [a0ce0b1b3bb72d1f] 79,384,833 25109E7F
02.06-raw_data.mkv [ad2923540cb3addc] 120,951,425 E63BC337
02.07-machine_learning_is_mostly_data_preparation.mkv [744d2dc2570e6e57] 30,526,743 DB013CA5
02.08-common_data_preparation_tasks-data_cleansing.mkv [98af017027055b26] 167,969,011 1E6EF3DD
02.09-common_data_preparation_tasks-feature_selection.mkv [5a8be69d0606e15] 54,170,861 B10E125B
02.10-common_data_preparation_tasks-data_transforms.mkv [e1da8b0ed9692f01] 10,967,835 2304877F
02.11-common_data_preparation_tasks-feature_engineering.mkv [90e2a19af193e417] 141,162,267 71DE89D0
02.12-common_data_preparation_tasks-dimensionality_reduction.mkv [e2f986ad9cda1bfa] 9,584,140 0DAE2D29
02.13-data_leakage.mkv [7dc979dbeaee26e5] 11,821,330 0B5953BA
02.14-problem_with_naive_data_preparation.mkv [6f61fbebe704f4fc] 149,862,051 372E443D
02.15-case_study_data_leakage_train__test__split_naive_approach.mkv [559381d7fea0ecbf] 49,169,628 F6686FAB
02.16-case_study_data_leakage_train__test__split_correct_approach.mkv [f90de982f2de9c09] 28,548,152 96B332E5
02.17-case_study_data_leakage_k-fold_naive_approach.mkv [3e3034b6505c209b] 41,481,637 21806136
02.18-case_study_data_leakage_k-fold_correct_approach.mkv [7e2cff35f825bc39] 37,121,061 DA4D26D4
03.01-data_cleansing_overview.mkv [6f094a0aec06c225] 167,419,430 619D628C
03.02-identify_columns_that_contain_a_single_value.mkv [80f165d386f40a80] 19,005,405 446693CB
03.03-identify_columns_with_few_values.mkv [cac17778eaa4b8c6] 32,705,727 AE18421F
03.04-remove_columns_with_low_variance.mkv [4f04a8269fe9c3cb] 30,550,435 A7465D1C
03.05-identify_and_remove_rows_that_contain_duplicate_data.mkv [6a91b92cb16b7622] 116,168,228 7FBEE09A
03.06-defining_outliers.mkv [b1653710db394903] 102,418,418 FE2C00AA
03.07-remove_outliers-the_standard_deviation_approach.mkv [fe13c31f3a03ee2c] 52,426,575 4FC32985
03.08-remove_outliers-the_iqr_approach.mkv [172c5feba5a4f93c] 42,653,357 EAFE5942
03.09-automatic_outlier_detection.mkv [73236e980bbfe76b] 52,657,917 BB2958E3
03.10-mark_missing_values.mkv [35c1511a645e46e] 62,942,856 5551701B
03.11-remove_rows_with_missing_values.mkv [c9bd672b4c5bef13] 29,089,147 6ABC1093
03.12-statistical_imputation.mkv [ac02f85082f4e118] 6,266,701 0EC0B33E
03.13-mean_value_imputation.mkv [cfa05ff6573d76fb] 43,893,412 129AF72B
03.14-simple_imputer_with_model_evaluation.mkv [6f1232e369def063] 22,289,208 0963BDE7
03.15-compare_different_statistical_imputation_strategies.mkv [b5350d6c6a69d1bf] 26,545,189 287F7AD0
03.16-k-nearest_neighbors_imputation.mkv [97264724a469aed] 46,546,262 F244B704
03.17-knnimputer_and_model_evaluation.mkv [59fdcce7bfd00feb] 36,002,433 5F4A159C
03.18-iterative_imputation.mkv [615d4f1bb463f77d] 39,438,883 57CE87E7
03.19-iterativeimputer_and_model_evaluation.mkv [5450461217f3412c] 19,302,103 DABA81EA
03.20-iterativeimputer_and_different_imputation_order.mkv [b12dfdd13ff10ae1] 24,144,987 16F0901C
04.01-feature_selection_introduction.mkv [d6d93d29010cfed6] 212,980,631 0406EF8A
04.02-feature_selection_defined.mkv [a036c0510b931e95] 12,456,808 C422A1FB
04.03-statistics_for_feature_selection.mkv [705357fdb4aa6a6d] 109,362,154 7B85AA0E
04.04-loading_a_categorical_dataset.mkv [a7bc2e913040af87] 28,990,616 933EAB07
04.05-encode_the_dataset_for_modelling.mkv [bae3b5fa57030de8] 26,234,382 7AC94CCD
04.06-chi-squared.mkv [4cdb46187d258981] 18,334,587 BD813743
04.07-mutual_information.mkv [fe3d4aa0e786ee98] 19,080,596 A565F28A
04.08-modeling_with_selected_categorical_features.mkv [7839078840052662] 39,245,739 67356ECB
04.09-feature_selection_with_anova_on_numerical_input.mkv [662286cb78d247f5] 43,806,597 89DCD4FF
04.10-feature_selection_with_mutual_information.mkv [d3fb91f12ccff7fb] 19,088,512 EC292276
04.11-modeling_with_selected_numerical_features.mkv [ca4c9329ea53a77e] 27,246,495 CCB9EF9A
04.12-tuning_a_number_of_selected_features.mkv [f7fb9ad819fd96b9] 39,813,815 B97C0809
04.13-select_features_for_numerical_output.mkv [7d79bd84ce14f6ff] 23,785,625 D493B432
04.14-linear_correlation_with_correlation_statistics.mkv [28bc840d2fdb1e3f] 27,455,204 30247B29
04.15-linear_correlation_with_mutual_information.mkv [66c698e65aad8453] 30,802,438 1B79C3D3
04.16-baseline_and_model_built_using_correlation.mkv [26b47389def2f3fb] 37,470,856 F94C1E75
04.17-model_built_using_mutual_information_features.mkv [1383a795299042c5] 11,977,012 BF9B1649
04.18-tuning_number_of_selected_features.mkv [1697ccba5a15d355] 57,345,569 0CB80F98
04.19-recursive_feature_elimination.mkv [122686587d5b8da] 185,147,007 E911C891
04.20-rfe_for_classification.mkv [2417de469d248683] 53,509,811 C7231C42
04.21-rfe_for_regression.mkv [8399abf9da686fa4] 27,480,482 1AC26AAB
04.22-rfe_hyperparameters.mkv [7fe4760f7044ce1c] 34,220,431 57DCDD02
04.23-feature_ranking_for_rfe.mkv [a7dc5013ad24a3dc] 31,028,758 AF0EB1B1
04.24-feature_importance_scores_defined.mkv [29551f24463e4f] 196,258,752 923E743D
04.25-feature_importance_scores_linear_regression.mkv [9320de766c34f1ea] 36,856,040 3D23BF6E
04.26-feature_importance_scores_logistic_regression_and_cart.mkv [8c8f8a166411e129] 38,307,466 C108D308
04.27-feature_importance_scores_random_forests.mkv [63458b33e26e587f] 17,832,945 57AAEBFA
04.28-permutation_feature_importance.mkv [29ebf81922665301] 29,785,734 E3CB6D16
04.29-feature_selection_with_importance.mkv [3443ce910493b3bc] 44,410,447 E1178525
05.01-scale_numerical_data.mkv [22782ba2960e5ee8] 11,600,894 E50D88E5
05.02-diabetes_dataset_for_scaling.mkv [c72e23258952355b] 24,155,036 BABF6327
05.03-minmaxscaler_transform.mkv [50df64fa755a8ac1] 25,431,078 AC64872A
05.04-standardscaler_transform.mkv [fe99282177c7f3e7] 29,885,853 803CB74A
05.05-robust_scaling_data.mkv [bd610d816bb3dce7] 44,550,654 637B8A8F
05.06-robust_scaler_applied_to_dataset.mkv [7842f6eb82359957] 23,696,418 455A6033
05.07-explore_robust_scaler_range.mkv [d12287bf9775d83c] 15,630,561 C11B1F3C
05.08-nominal_and_ordinal_variables.mkv [7bafea9ac48edd9c] 316,302,042 DC4FA764
05.09-ordinal_encoding.mkv [bc163cea8ac8759b] 17,839,280 6B63ABD9
05.10-one-hot_encoding_defined.mkv [88319536116459f9] 3,880,740 BB04B4AD
05.11-one-hot_encoding.mkv [29562ac57e64e4] 18,106,821 A9D47B9E
05.12-dummy_variable_encoding.mkv [85ee945563ec3754] 18,311,290 F04006C6
05.13-ordinal_encoder_transform_on_breast_cancer_dataset.mkv [22ea6054e34e6ee8] 47,879,193 BF6AEC7B
05.14-make_distributions_more_gaussian.mkv [bc6f692317438685] 9,311,735 5183F41E
05.15-power_transform_on_contrived_dataset.mkv [2234b16ca2164a6e] 22,376,388 E95F48F6
05.16-power_transform_on_sonar_dataset.mkv [546c2a18b68bf6fb] 30,399,819 0D1B9565
05.17-box-cox_on_sonar_dataset.mkv [175e53391f58c0de] 33,320,553 D4EA1529
05.18-yeo-johnson_on_sonar_dataset.mkv [1633dbd27c30d960] 27,289,785 37864A77
05.19-polynomial_features.mkv [bea60187a4033619] 160,292,017 05627EAB
05.20-effect_of_polynomial_degrees.mkv [d96dd75d54997cef] 20,184,746 197692CA
06.01-transforming_different_data_types.mkv [cbb8de3df675ea35] 24,559,318 957CA6C8
06.02-the_columntransformer.mkv [77cc16f0a2915d0b] 29,594,175 A8219D15
06.03-the_columntransformer_on_abalone_dataset.mkv [c1d5ebd9bb1b9840] 37,041,371 C0AAFDB1
06.04-manually_transform_target_variable.mkv [b9dbc86d9de8d7c7] 25,722,029 0377EC49
06.05-automatically_transform_target_variable.mkv [7759a5930b15c23f] 57,078,212 0200569F
06.06-challenge_of_preparing_new_data_for_a_model.mkv [300b3df9dec412ee] 258,900,775 3D624E7A
06.07-save_model_and_data_scaler.mkv [734a4322cc91952b] 42,341,870 A8977AE9
06.08-load_and_apply_saved_scalers.mkv [3543e3be6a831307] 18,814,309 C15A0313
07.01-curse_of_dimensionality.mkv [970b0261266662c1] 15,022,150 A5093163
07.02-techniques_for_dimensionality_reduction.mkv [d61c5adcf246e834] 102,228,987 9B08F09E
07.03-linear_discriminant_analysis.mkv [f042be4d2cb3ef54] 20,197,281 3B363E8B
07.04-linear_discriminant_analysis_demonstrated.mkv [1837a93c47dcaa2f] 51,491,386 36B53F30
07.05-principal_component_analysis.mkv [5415fbfe28713e91] 62,650,559 62B0DF21
9781803239040_Code.zip 814,860 BCF07D47
Total size: 6,288,755,353
Video files
Sample
packt.data.cleansing.master.class.in.python-xqzt-sample.mkv 84,531,309 FF28221D
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