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PacktPub - Data Cleansing Master Class in Python

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File Name
Size
Section 5/05.08-nominal_and_ordinal_variables.mkv
302 MB
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377 B
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1.8 kB
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204 B
Section 1/01.01-course_introduction.mkv
153 MB
Section 1/01.02-course_structure.mkv
157 MB
Section 1/01.03-is_this_course_right_for_you.mkv
4.3 MB
Section 2/02.01-introducing_data_preparation.mkv
277 MB
Section 2/02.02-the_machine_learning_process.mkv
91 MB
Section 2/02.03-data_preparation_defined.mkv
252 MB
Section 2/02.04-choosing_a_data_preparation_technique.mkv
264 MB
Section 2/02.05-what_is_data_in_machine_learning.mkv
76 MB
Section 2/02.06-raw_data.mkv
115 MB
Section 2/02.07-machine_learning_is_mostly_data_preparation.mkv
29 MB
Section 2/02.08-common_data_preparation_tasks-data_cleansing.mkv
160 MB
Section 2/02.09-common_data_preparation_tasks-feature_selection.mkv
52 MB
Section 2/02.10-common_data_preparation_tasks-data_transforms.mkv
10 MB
Section 2/02.11-common_data_preparation_tasks-feature_engineering.mkv
135 MB
Section 2/02.12-common_data_preparation_tasks-dimensionality_reduction.mkv
9.1 MB
Section 2/02.13-data_leakage.mkv
11 MB
Section 2/02.14-problem_with_naive_data_preparation.mkv
143 MB
Section 2/02.15-case_study_data_leakage_train__test__split_naive_approach.mkv
47 MB
Section 2/02.16-case_study_data_leakage_train__test__split_correct_approach.mkv
27 MB
Section 2/02.17-case_study_data_leakage_k-fold_naive_approach.mkv
40 MB
Section 2/02.18-case_study_data_leakage_k-fold_correct_approach.mkv
35 MB
Section 3/03.01-data_cleansing_overview.mkv
160 MB
Section 3/03.02-identify_columns_that_contain_a_single_value.mkv
18 MB
Section 3/03.03-identify_columns_with_few_values.mkv
31 MB
Section 3/03.04-remove_columns_with_low_variance.mkv
29 MB
Section 3/03.05-identify_and_remove_rows_that_contain_duplicate_data.mkv
111 MB
Section 3/03.06-defining_outliers.mkv
98 MB
Section 3/03.07-remove_outliers-the_standard_deviation_approach.mkv
50 MB
Section 3/03.08-remove_outliers-the_iqr_approach.mkv
41 MB
Section 3/03.09-automatic_outlier_detection.mkv
50 MB
Section 3/03.10-mark_missing_values.mkv
60 MB
Section 3/03.11-remove_rows_with_missing_values.mkv
28 MB
Section 3/03.12-statistical_imputation.mkv
6.0 MB
Section 3/03.13-mean_value_imputation.mkv
42 MB
Section 3/03.14-simple_imputer_with_model_evaluation.mkv
21 MB
Section 3/03.15-compare_different_statistical_imputation_strategies.mkv
25 MB
Section 3/03.16-k-nearest_neighbors_imputation.mkv
44 MB
Section 3/03.17-knnimputer_and_model_evaluation.mkv
34 MB
Section 3/03.18-iterative_imputation.mkv
38 MB
Section 3/03.19-iterativeimputer_and_model_evaluation.mkv
18 MB
Section 3/03.20-iterativeimputer_and_different_imputation_order.mkv
23 MB
Section 4/04.01-feature_selection_introduction.mkv
203 MB
Section 4/04.02-feature_selection_defined.mkv
12 MB
Section 4/04.03-statistics_for_feature_selection.mkv
104 MB
Section 4/04.04-loading_a_categorical_dataset.mkv
28 MB
Section 4/04.05-encode_the_dataset_for_modelling.mkv
25 MB
Section 4/04.06-chi-squared.mkv
18 MB
Section 4/04.07-mutual_information.mkv
18 MB
Section 4/04.08-modeling_with_selected_categorical_features.mkv
37 MB
Section 4/04.09-feature_selection_with_anova_on_numerical_input.mkv
42 MB
Section 4/04.10-feature_selection_with_mutual_information.mkv
18 MB
Section 4/04.11-modeling_with_selected_numerical_features.mkv
26 MB
Section 4/04.12-tuning_a_number_of_selected_features.mkv
38 MB
Section 4/04.13-select_features_for_numerical_output.mkv
23 MB
Section 4/04.14-linear_correlation_with_correlation_statistics.mkv
26 MB
Section 4/04.15-linear_correlation_with_mutual_information.mkv
29 MB
Section 4/04.16-baseline_and_model_built_using_correlation.mkv
36 MB
Section 4/04.17-model_built_using_mutual_information_features.mkv
11 MB
Section 4/04.18-tuning_number_of_selected_features.mkv
55 MB
Section 4/04.19-recursive_feature_elimination.mkv
177 MB
Section 4/04.20-rfe_for_classification.mkv
51 MB
Section 4/04.21-rfe_for_regression.mkv
26 MB
Section 4/04.22-rfe_hyperparameters.mkv
33 MB
Section 4/04.23-feature_ranking_for_rfe.mkv
30 MB
Section 4/04.24-feature_importance_scores_defined.mkv
187 MB
Section 4/04.25-feature_importance_scores_linear_regression.mkv
35 MB
Section 4/04.26-feature_importance_scores_logistic_regression_and_cart.mkv
36 MB
Section 4/04.27-feature_importance_scores_random_forests.mkv
17 MB
Section 4/04.28-permutation_feature_importance.mkv
28 MB
Section 4/04.29-feature_selection_with_importance.mkv
42 MB
Section 5/05.01-scale_numerical_data.mkv
11 MB
Section 5/05.02-diabetes_dataset_for_scaling.mkv
23 MB
Section 5/05.03-minmaxscaler_transform.mkv
24 MB
Section 5/05.04-standardscaler_transform.mkv
28 MB
Section 5/05.05-robust_scaling_data.mkv
42 MB
Section 5/05.06-robust_scaler_applied_to_dataset.mkv
23 MB
Section 5/05.07-explore_robust_scaler_range.mkv
15 MB
Exercises Files.zip
304 kB
Section 5/05.09-ordinal_encoding.mkv
17 MB
Section 5/05.10-one-hot_encoding_defined.mkv
3.7 MB
Section 5/05.11-one-hot_encoding.mkv
17 MB
Section 5/05.12-dummy_variable_encoding.mkv
18 MB
Section 5/05.13-ordinal_encoder_transform_on_breast_cancer_dataset.mkv
46 MB
Section 5/05.14-make_distributions_more_gaussian.mkv
8.9 MB
Section 5/05.15-power_transform_on_contrived_dataset.mkv
21 MB
Section 5/05.16-power_transform_on_sonar_dataset.mkv
29 MB
Section 5/05.17-box-cox_on_sonar_dataset.mkv
32 MB
Section 5/05.18-yeo-johnson_on_sonar_dataset.mkv
26 MB
Section 5/05.19-polynomial_features.mkv
153 MB
Section 5/05.20-effect_of_polynomial_degrees.mkv
19 MB
Section 6/06.01-transforming_different_data_types.mkv
23 MB
Section 6/06.02-the_columntransformer.mkv
28 MB
Section 6/06.03-the_columntransformer_on_abalone_dataset.mkv
35 MB
Section 6/06.04-manually_transform_target_variable.mkv
24 MB
Section 6/06.05-automatically_transform_target_variable.mkv
54 MB
Section 6/06.06-challenge_of_preparing_new_data_for_a_model.mkv
247 MB
Section 6/06.07-save_model_and_data_scaler.mkv
40 MB
Section 6/06.08-load_and_apply_saved_scalers.mkv
18 MB
Section 7/07.01-curse_of_dimensionality.mkv
14 MB
Section 7/07.02-techniques_for_dimensionality_reduction.mkv
98 MB
Section 7/07.03-linear_discriminant_analysis.mkv
19 MB
Section 7/07.04-linear_discriminant_analysis_demonstrated.mkv
49 MB
Section 7/07.05-principal_component_analysis.mkv
60 MB