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PacktPub | Data Cleansing Master Class in Python [...
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PacktPub | Data Cleansing Master Class in Python [Video] [FCO] Torrent content (File list)
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| 0. Websites you may like/How you can help our Group!.txt 0.2 KB |
| Exercises Files.zip 304.4 KB |
| Section 1/01.01-course_introduction.mkv 152.9 MB |
| Section 1/01.02-course_structure.mkv 157.2 MB |
| Section 1/01.03-is_this_course_right_for_you.mkv 4.3 MB |
| Section 2/02.01-introducing_data_preparation.mkv 277.1 MB |
| Section 2/02.02-the_machine_learning_process.mkv 90.8 MB |
| Section 2/02.03-data_preparation_defined.mkv 251.9 MB |
| Section 2/02.04-choosing_a_data_preparation_technique.mkv 264.0 MB |
| Section 2/02.05-what_is_data_in_machine_learning.mkv 75.7 MB |
| Section 2/02.06-raw_data.mkv 115.3 MB |
| Section 2/02.07-machine_learning_is_mostly_data_preparation.mkv 29.1 MB |
| Section 2/02.08-common_data_preparation_tasks-data_cleansing.mkv 160.2 MB |
| Section 2/02.09-common_data_preparation_tasks-feature_selection.mkv 51.7 MB |
| Section 2/02.10-common_data_preparation_tasks-data_transforms.mkv 10.5 MB |
| Section 2/02.11-common_data_preparation_tasks-feature_engineering.mkv 134.6 MB |
| Section 2/02.12-common_data_preparation_tasks-dimensionality_reduction.mkv 9.1 MB |
| Section 2/02.13-data_leakage.mkv 11.3 MB |
| Section 2/02.14-problem_with_naive_data_preparation.mkv 142.9 MB |
| Section 2/02.15-case_study_data_leakage_train__test__split_naive_approach.mkv 46.9 MB |
| Section 2/02.16-case_study_data_leakage_train__test__split_correct_approach.mkv 27.2 MB |
| Section 2/02.17-case_study_data_leakage_k-fold_naive_approach.mkv 39.6 MB |
| Section 2/02.18-case_study_data_leakage_k-fold_correct_approach.mkv 35.4 MB |
| Section 3/03.01-data_cleansing_overview.mkv 159.7 MB |
| Section 3/03.02-identify_columns_that_contain_a_single_value.mkv 18.1 MB |
| Section 3/03.03-identify_columns_with_few_values.mkv 31.2 MB |
| Section 3/03.04-remove_columns_with_low_variance.mkv 29.1 MB |
| Section 3/03.05-identify_and_remove_rows_that_contain_duplicate_data.mkv 110.8 MB |
| Section 3/03.06-defining_outliers.mkv 97.7 MB |
| Section 3/03.07-remove_outliers-the_standard_deviation_approach.mkv 50.0 MB |
| Section 3/03.08-remove_outliers-the_iqr_approach.mkv 40.7 MB |
| Section 3/03.09-automatic_outlier_detection.mkv 50.2 MB |
| Section 3/03.10-mark_missing_values.mkv 60.0 MB |
| Section 3/03.11-remove_rows_with_missing_values.mkv 27.7 MB |
| Section 3/03.12-statistical_imputation.mkv 6.0 MB |
| Section 3/03.13-mean_value_imputation.mkv 41.9 MB |
| Section 3/03.14-simple_imputer_with_model_evaluation.mkv 21.3 MB |
| Section 3/03.15-compare_different_statistical_imputation_strategies.mkv 25.3 MB |
| Section 3/03.16-k-nearest_neighbors_imputation.mkv 44.4 MB |
| Section 3/03.17-knnimputer_and_model_evaluation.mkv 34.3 MB |
| Section 3/03.18-iterative_imputation.mkv 37.6 MB |
| Section 3/03.19-iterativeimputer_and_model_evaluation.mkv 18.4 MB |
| Section 3/03.20-iterativeimputer_and_different_imputation_order.mkv 23.0 MB |
| Section 4/04.01-feature_selection_introduction.mkv 203.1 MB |
| Section 4/04.02-feature_selection_defined.mkv 11.9 MB |
| Section 4/04.03-statistics_for_feature_selection.mkv 104.3 MB |
| Section 4/04.04-loading_a_categorical_dataset.mkv 27.6 MB |
| Section 4/04.05-encode_the_dataset_for_modelling.mkv 25.0 MB |
| Section 4/04.06-chi-squared.mkv 17.5 MB |
| Section 4/04.07-mutual_information.mkv 18.2 MB |
| Section 4/04.08-modeling_with_selected_categorical_features.mkv 37.4 MB |
| Section 4/04.09-feature_selection_with_anova_on_numerical_input.mkv 41.8 MB |
| Section 4/04.10-feature_selection_with_mutual_information.mkv 18.2 MB |
| Section 4/04.11-modeling_with_selected_numerical_features.mkv 26.0 MB |
| Section 4/04.12-tuning_a_number_of_selected_features.mkv 38.0 MB |
| Section 4/04.13-select_features_for_numerical_output.mkv 22.7 MB |
| Section 4/04.14-linear_correlation_with_correlation_statistics.mkv 26.2 MB |
| Section 4/04.15-linear_correlation_with_mutual_information.mkv 29.4 MB |
| Section 4/04.16-baseline_and_model_built_using_correlation.mkv 35.7 MB |
| Section 4/04.17-model_built_using_mutual_information_features.mkv 11.4 MB |
| Section 4/04.18-tuning_number_of_selected_features.mkv 54.7 MB |
| Section 4/04.19-recursive_feature_elimination.mkv 176.6 MB |
| Section 4/04.20-rfe_for_classification.mkv 51.0 MB |
| Section 4/04.21-rfe_for_regression.mkv 26.2 MB |
| Section 4/04.22-rfe_hyperparameters.mkv 32.6 MB |
| Section 4/04.23-feature_ranking_for_rfe.mkv 29.6 MB |
| Section 4/04.24-feature_importance_scores_defined.mkv 187.2 MB |
| Section 4/04.25-feature_importance_scores_linear_regression.mkv 35.1 MB |
| Section 4/04.26-feature_importance_scores_logistic_regression_and_cart.mkv 36.5 MB |
| Section 4/04.27-feature_importance_scores_random_forests.mkv 17.0 MB |
| Section 4/04.28-permutation_feature_importance.mkv 28.4 MB |
| Section 4/04.29-feature_selection_with_importance.mkv 42.4 MB |
| Section 5/05.01-scale_numerical_data.mkv 11.1 MB |
| Section 5/05.02-diabetes_dataset_for_scaling.mkv 23.0 MB |
| Section 5/05.03-minmaxscaler_transform.mkv 24.3 MB |
| Section 5/05.04-standardscaler_transform.mkv 28.5 MB |
| Section 5/05.05-robust_scaling_data.mkv 42.5 MB |
| Section 5/05.06-robust_scaler_applied_to_dataset.mkv 22.6 MB |
| Section 5/05.07-explore_robust_scaler_range.mkv 14.9 MB |
| Section 5/05.08-nominal_and_ordinal_variables.mkv 301.6 MB |
| Section 5/05.09-ordinal_encoding.mkv 17.0 MB |
| Section 5/05.10-one-hot_encoding_defined.mkv 3.7 MB |
| Section 5/05.11-one-hot_encoding.mkv 17.3 MB |
| Section 5/05.12-dummy_variable_encoding.mkv 17.5 MB |
| Section 5/05.13-ordinal_encoder_transform_on_breast_cancer_dataset.mkv 45.7 MB |
| Section 5/05.14-make_distributions_more_gaussian.mkv 8.9 MB |
| Section 5/05.15-power_transform_on_contrived_dataset.mkv 21.3 MB |
| Section 5/05.16-power_transform_on_sonar_dataset.mkv 29.0 MB |
| Section 5/05.17-box-cox_on_sonar_dataset.mkv 31.8 MB |
| Section 5/05.18-yeo-johnson_on_sonar_dataset.mkv 26.0 MB |
| Section 5/05.19-polynomial_features.mkv 152.9 MB |
| Section 5/05.20-effect_of_polynomial_degrees.mkv 19.2 MB |
| Section 6/06.01-transforming_different_data_types.mkv 23.4 MB |
| Section 6/06.02-the_columntransformer.mkv 28.2 MB |
| Section 6/06.03-the_columntransformer_on_abalone_dataset.mkv 35.3 MB |
| Section 6/06.04-manually_transform_target_variable.mkv 24.5 MB |
| Section 6/06.05-automatically_transform_target_variable.mkv 54.4 MB |
| Section 6/06.06-challenge_of_preparing_new_data_for_a_model.mkv 246.9 MB |
| Section 6/06.07-save_model_and_data_scaler.mkv 40.4 MB |
| Section 6/06.08-load_and_apply_saved_scalers.mkv 17.9 MB |
| Section 7/07.01-curse_of_dimensionality.mkv 14.3 MB |
| Section 7/07.02-techniques_for_dimensionality_reduction.mkv 97.5 MB |
| Section 7/07.03-linear_discriminant_analysis.mkv 19.3 MB |
| Section 7/07.04-linear_discriminant_analysis_demonstrated.mkv 49.1 MB |
| Section 7/07.05-principal_component_analysis.mkv 59.7 MB |
- Torrent indexed: 3 years
- Torrent updated: Friday 7th of January 2022 05:00:07 PM
- Torrent hash: DFA70D9E9D4CE97BCE01A92EFC326505231DDBB8
- Torrent size: 5.9 GB
- Torrent category: Tutorials
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