Packt Data Cleansing Master Class in Python-XQZT

Another exquisit release
Packt.Data.Cleansing.Master.Class.in.Python-XQZT
     Title: Data Cleansing Master Class in Python
 Publisher: Packt
  Category: Data
      Size: 6079M
     Files: 11F
      Date: 2021-12-21
  Course #: 9781803239040
 Published: Packt
   Updated: N/A
       URL: https://www.packtpub.com/video/data/9781803239040
    Author: Mike West
  Duration: 3 hours 33 minutes
 Exer/Code: [X]
Installation:
Unpack that shit, run that shit
Description:
A step-by-step complete guide to become a machine learning

153M	01.01-course_introduction.mkv
158M	01.02-course_structure.mkv
4.3M	01.03-is_this_course_right_for_you.mkv
278M	02.01-introducing_data_preparation.mkv
91M	02.02-the_machine_learning_process.mkv
252M	02.03-data_preparation_defined.mkv
265M	02.04-choosing_a_data_preparation_technique.mkv
76M	02.05-what_is_data_in_machine_learning.mkv
116M	02.06-raw_data.mkv
30M	02.07-machine_learning_is_mostly_data_preparation.mkv
161M	02.08-common_data_preparation_tasks-data_cleansing.mkv
52M	02.09-common_data_preparation_tasks-feature_selection.mkv
11M	02.10-common_data_preparation_tasks-data_transforms.mkv
135M	02.11-common_data_preparation_tasks-feature_engineering.mkv
9.2M	02.12-common_data_preparation_tasks-dimensionality_reduction.mkv
12M	02.13-data_leakage.mkv
143M	02.14-problem_with_naive_data_preparation.mkv
47M	02.15-case_study_data_leakage_train__test__split_naive_approach.mkv
28M	02.16-case_study_data_leakage_train__test__split_correct_approach.mkv
40M	02.17-case_study_data_leakage_k-fold_naive_approach.mkv
36M	02.18-case_study_data_leakage_k-fold_correct_approach.mkv
160M	03.01-data_cleansing_overview.mkv
19M	03.02-identify_columns_that_contain_a_single_value.mkv
32M	03.03-identify_columns_with_few_values.mkv
30M	03.04-remove_columns_with_low_variance.mkv
111M	03.05-identify_and_remove_rows_that_contain_duplicate_data.mkv
98M	03.06-defining_outliers.mkv
50M	03.07-remove_outliers-the_standard_deviation_approach.mkv
41M	03.08-remove_outliers-the_iqr_approach.mkv
51M	03.09-automatic_outlier_detection.mkv
61M	03.10-mark_missing_values.mkv
28M	03.11-remove_rows_with_missing_values.mkv
6.0M	03.12-statistical_imputation.mkv
42M	03.13-mean_value_imputation.mkv
22M	03.14-simple_imputer_with_model_evaluation.mkv
26M	03.15-compare_different_statistical_imputation_strategies.mkv
45M	03.16-k-nearest_neighbors_imputation.mkv
35M	03.17-knnimputer_and_model_evaluation.mkv
38M	03.18-iterative_imputation.mkv
19M	03.19-iterativeimputer_and_model_evaluation.mkv
24M	03.20-iterativeimputer_and_different_imputation_order.mkv
204M	04.01-feature_selection_introduction.mkv
12M	04.02-feature_selection_defined.mkv
105M	04.03-statistics_for_feature_selection.mkv
28M	04.04-loading_a_categorical_dataset.mkv
26M	04.05-encode_the_dataset_for_modelling.mkv
18M	04.06-chi-squared.mkv
19M	04.07-mutual_information.mkv
38M	04.08-modeling_with_selected_categorical_features.mkv
42M	04.09-feature_selection_with_anova_on_numerical_input.mkv
19M	04.10-feature_selection_with_mutual_information.mkv
26M	04.11-modeling_with_selected_numerical_features.mkv
38M	04.12-tuning_a_number_of_selected_features.mkv
23M	04.13-select_features_for_numerical_output.mkv
27M	04.14-linear_correlation_with_correlation_statistics.mkv
30M	04.15-linear_correlation_with_mutual_information.mkv
36M	04.16-baseline_and_model_built_using_correlation.mkv
12M	04.17-model_built_using_mutual_information_features.mkv
55M	04.18-tuning_number_of_selected_features.mkv
177M	04.19-recursive_feature_elimination.mkv
52M	04.20-rfe_for_classification.mkv
27M	04.21-rfe_for_regression.mkv
33M	04.22-rfe_hyperparameters.mkv
30M	04.23-feature_ranking_for_rfe.mkv
188M	04.24-feature_importance_scores_defined.mkv
36M	04.25-feature_importance_scores_linear_regression.mkv
37M	04.26-feature_importance_scores_logistic_regression_and_cart.mkv
18M	04.27-feature_importance_scores_random_forests.mkv
29M	04.28-permutation_feature_importance.mkv
43M	04.29-feature_selection_with_importance.mkv
12M	05.01-scale_numerical_data.mkv
24M	05.02-diabetes_dataset_for_scaling.mkv
25M	05.03-minmaxscaler_transform.mkv
29M	05.04-standardscaler_transform.mkv
43M	05.05-robust_scaling_data.mkv
23M	05.06-robust_scaler_applied_to_dataset.mkv
15M	05.07-explore_robust_scaler_range.mkv
302M	05.08-nominal_and_ordinal_variables.mkv
18M	05.09-ordinal_encoding.mkv
3.8M	05.10-one-hot_encoding_defined.mkv
18M	05.11-one-hot_encoding.mkv
18M	05.12-dummy_variable_encoding.mkv
46M	05.13-ordinal_encoder_transform_on_breast_cancer_dataset.mkv
8.9M	05.14-make_distributions_more_gaussian.mkv
22M	05.15-power_transform_on_contrived_dataset.mkv
29M	05.16-power_transform_on_sonar_dataset.mkv
32M	05.17-box-cox_on_sonar_dataset.mkv
27M	05.18-yeo-johnson_on_sonar_dataset.mkv
153M	05.19-polynomial_features.mkv
20M	05.20-effect_of_polynomial_degrees.mkv
24M	06.01-transforming_different_data_types.mkv
29M	06.02-the_columntransformer.mkv
36M	06.03-the_columntransformer_on_abalone_dataset.mkv
25M	06.04-manually_transform_target_variable.mkv
55M	06.05-automatically_transform_target_variable.mkv
247M	06.06-challenge_of_preparing_new_data_for_a_model.mkv
41M	06.07-save_model_and_data_scaler.mkv
18M	06.08-load_and_apply_saved_scalers.mkv
15M	07.01-curse_of_dimensionality.mkv
98M	07.02-techniques_for_dimensionality_reduction.mkv
20M	07.03-linear_discriminant_analysis.mkv
50M	07.04-linear_discriminant_analysis_demonstrated.mkv
60M	07.05-principal_component_analysis.mkv
796K	9781803239040_Code.zip
5.9G	total