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种子名称： PacktPub - Data Cleansing Master Class in Python
文件类型： 视频
文件数目： 103个文件
文件大小： 5.86 GB
收录时间： 2022-8-21 05:36
已经下载： 3次
资源热度： 182
最近下载： 2024-11-14 23:33

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PacktPub - Data Cleansing Master Class in Python.torrent

Section 1/01.01-course_introduction.mkv152.85MB

Section 1/01.02-course_structure.mkv157.17MB

Section 1/01.03-is_this_course_right_for_you.mkv4.25MB

Section 2/02.01-introducing_data_preparation.mkv277.14MB

Section 2/02.02-the_machine_learning_process.mkv90.77MB

Section 2/02.03-data_preparation_defined.mkv251.94MB

Section 2/02.04-choosing_a_data_preparation_technique.mkv264.02MB

Section 2/02.05-what_is_data_in_machine_learning.mkv75.71MB

Section 2/02.06-raw_data.mkv115.35MB

Section 2/02.07-machine_learning_is_mostly_data_preparation.mkv29.11MB

Section 2/02.08-common_data_preparation_tasks-data_cleansing.mkv160.19MB

Section 2/02.09-common_data_preparation_tasks-feature_selection.mkv51.66MB

Section 2/02.10-common_data_preparation_tasks-data_transforms.mkv10.46MB

Section 2/02.11-common_data_preparation_tasks-feature_engineering.mkv134.62MB

Section 2/02.12-common_data_preparation_tasks-dimensionality_reduction.mkv9.14MB

Section 2/02.13-data_leakage.mkv11.27MB

Section 2/02.14-problem_with_naive_data_preparation.mkv142.92MB

Section 2/02.15-case_study_data_leakage_train__test__split_naive_approach.mkv46.89MB

Section 2/02.16-case_study_data_leakage_train__test__split_correct_approach.mkv27.23MB

Section 2/02.17-case_study_data_leakage_k-fold_naive_approach.mkv39.56MB

Section 2/02.18-case_study_data_leakage_k-fold_correct_approach.mkv35.4MB

Section 3/03.01-data_cleansing_overview.mkv159.66MB

Section 3/03.02-identify_columns_that_contain_a_single_value.mkv18.12MB

Section 3/03.03-identify_columns_with_few_values.mkv31.19MB

Section 3/03.04-remove_columns_with_low_variance.mkv29.14MB

Section 3/03.05-identify_and_remove_rows_that_contain_duplicate_data.mkv110.79MB

Section 3/03.06-defining_outliers.mkv97.67MB

Section 3/03.07-remove_outliers-the_standard_deviation_approach.mkv50MB

Section 3/03.08-remove_outliers-the_iqr_approach.mkv40.68MB

Section 3/03.09-automatic_outlier_detection.mkv50.22MB

Section 3/03.10-mark_missing_values.mkv60.03MB

Section 3/03.11-remove_rows_with_missing_values.mkv27.74MB

Section 3/03.12-statistical_imputation.mkv5.98MB

Section 3/03.13-mean_value_imputation.mkv41.86MB

Section 3/03.14-simple_imputer_with_model_evaluation.mkv21.26MB

Section 3/03.15-compare_different_statistical_imputation_strategies.mkv25.32MB

Section 3/03.16-k-nearest_neighbors_imputation.mkv44.39MB

Section 3/03.17-knnimputer_and_model_evaluation.mkv34.33MB

Section 3/03.18-iterative_imputation.mkv37.61MB

Section 3/03.19-iterativeimputer_and_model_evaluation.mkv18.41MB

Section 3/03.20-iterativeimputer_and_different_imputation_order.mkv23.03MB

Section 4/04.01-feature_selection_introduction.mkv203.11MB

Section 4/04.02-feature_selection_defined.mkv11.88MB

Section 4/04.03-statistics_for_feature_selection.mkv104.3MB

Section 4/04.04-loading_a_categorical_dataset.mkv27.65MB

Section 4/04.05-encode_the_dataset_for_modelling.mkv25.02MB

Section 4/04.06-chi-squared.mkv17.49MB

Section 4/04.07-mutual_information.mkv18.2MB

Section 4/04.08-modeling_with_selected_categorical_features.mkv37.43MB

Section 4/04.09-feature_selection_with_anova_on_numerical_input.mkv41.78MB

Section 4/04.10-feature_selection_with_mutual_information.mkv18.2MB

Section 4/04.11-modeling_with_selected_numerical_features.mkv25.98MB

Section 4/04.12-tuning_a_number_of_selected_features.mkv37.97MB

Section 4/04.13-select_features_for_numerical_output.mkv22.68MB

Section 4/04.14-linear_correlation_with_correlation_statistics.mkv26.18MB

Section 4/04.15-linear_correlation_with_mutual_information.mkv29.38MB

Section 4/04.16-baseline_and_model_built_using_correlation.mkv35.73MB

Section 4/04.17-model_built_using_mutual_information_features.mkv11.42MB

Section 4/04.18-tuning_number_of_selected_features.mkv54.69MB

Section 4/04.19-recursive_feature_elimination.mkv176.57MB

Section 4/04.20-rfe_for_classification.mkv51.03MB

Section 4/04.21-rfe_for_regression.mkv26.21MB

Section 4/04.22-rfe_hyperparameters.mkv32.64MB

Section 4/04.23-feature_ranking_for_rfe.mkv29.59MB

Section 4/04.24-feature_importance_scores_defined.mkv187.17MB

Section 4/04.25-feature_importance_scores_linear_regression.mkv35.15MB

Section 4/04.26-feature_importance_scores_logistic_regression_and_cart.mkv36.53MB

Section 4/04.27-feature_importance_scores_random_forests.mkv17.01MB

Section 4/04.28-permutation_feature_importance.mkv28.41MB

Section 4/04.29-feature_selection_with_importance.mkv42.35MB

Section 5/05.01-scale_numerical_data.mkv11.06MB

Section 5/05.02-diabetes_dataset_for_scaling.mkv23.04MB

Section 5/05.03-minmaxscaler_transform.mkv24.25MB

Section 5/05.04-standardscaler_transform.mkv28.5MB

Section 5/05.05-robust_scaling_data.mkv42.49MB

Section 5/05.06-robust_scaler_applied_to_dataset.mkv22.6MB

Section 5/05.07-explore_robust_scaler_range.mkv14.91MB

Section 5/05.08-nominal_and_ordinal_variables.mkv301.65MB

Section 5/05.09-ordinal_encoding.mkv17.01MB

Section 5/05.10-one-hot_encoding_defined.mkv3.7MB

Section 5/05.11-one-hot_encoding.mkv17.27MB

Section 5/05.12-dummy_variable_encoding.mkv17.46MB

Section 5/05.13-ordinal_encoder_transform_on_breast_cancer_dataset.mkv45.66MB

Section 5/05.14-make_distributions_more_gaussian.mkv8.88MB

Section 5/05.15-power_transform_on_contrived_dataset.mkv21.34MB

Section 5/05.16-power_transform_on_sonar_dataset.mkv28.99MB

Section 5/05.17-box-cox_on_sonar_dataset.mkv31.78MB

Section 5/05.18-yeo-johnson_on_sonar_dataset.mkv26.03MB

Section 5/05.19-polynomial_features.mkv152.87MB

Section 5/05.20-effect_of_polynomial_degrees.mkv19.25MB

Section 6/06.01-transforming_different_data_types.mkv23.42MB

Section 6/06.02-the_columntransformer.mkv28.22MB

Section 6/06.03-the_columntransformer_on_abalone_dataset.mkv35.33MB

Section 6/06.04-manually_transform_target_variable.mkv24.53MB

Section 6/06.05-automatically_transform_target_variable.mkv54.43MB

Section 6/06.06-challenge_of_preparing_new_data_for_a_model.mkv246.91MB

Section 6/06.07-save_model_and_data_scaler.mkv40.38MB

Section 6/06.08-load_and_apply_saved_scalers.mkv17.94MB

Section 7/07.01-curse_of_dimensionality.mkv14.33MB

Section 7/07.02-techniques_for_dimensionality_reduction.mkv97.49MB

Section 7/07.03-linear_discriminant_analysis.mkv19.26MB

Section 7/07.04-linear_discriminant_analysis_demonstrated.mkv49.11MB

Section 7/07.05-principal_component_analysis.mkv59.75MB