PacktPub Data Cleansing Master Class in Python | 542C9944D3404A8BABBF129A40E4E0982D996F97

PacktPub Data Cleansing Master Class in Python

Name	PacktPub - Data Cleansing Master Class in Python	DOWNLOAD Copy Link
Total Size	6.3 GB
Total Files	207
Last Seen	2024-11-07 01:22
Hash	542C9944D3404A8BABBF129A40E4E0982D996F97

/
Exercises Files.zip	311.7 KB
/.pad/
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/Section 1/
01.01-course_introduction.mkv	160.3 MB
01.02-course_structure.mkv	164.8 MB
01.03-is_this_course_right_for_you.mkv	4.5 MB
/Section 2/
02.01-introducing_data_preparation.mkv	290.6 MB
02.02-the_machine_learning_process.mkv	95.2 MB
02.03-data_preparation_defined.mkv	264.2 MB
02.04-choosing_a_data_preparation_technique.mkv	276.8 MB
02.05-what_is_data_in_machine_learning.mkv	79.4 MB
02.06-raw_data.mkv	121.0 MB
02.07-machine_learning_is_mostly_data_preparation.mkv	30.5 MB
02.08-common_data_preparation_tasks-data_cleansing.mkv	168.0 MB
02.09-common_data_preparation_tasks-feature_selection.mkv	54.2 MB
02.10-common_data_preparation_tasks-data_transforms.mkv	11.0 MB
02.11-common_data_preparation_tasks-feature_engineering.mkv	141.2 MB
02.12-common_data_preparation_tasks-dimensionality_reduction.mkv	9.6 MB
02.13-data_leakage.mkv	11.8 MB
02.14-problem_with_naive_data_preparation.mkv	149.9 MB
02.15-case_study_data_leakage_train__test__split_naive_approach.mkv	49.2 MB
02.16-case_study_data_leakage_train__test__split_correct_approach.mkv	28.5 MB
02.17-case_study_data_leakage_k-fold_naive_approach.mkv	41.5 MB
02.18-case_study_data_leakage_k-fold_correct_approach.mkv	37.1 MB
/Section 3/
03.01-data_cleansing_overview.mkv	167.4 MB
03.02-identify_columns_that_contain_a_single_value.mkv	19.0 MB
03.03-identify_columns_with_few_values.mkv	32.7 MB
03.04-remove_columns_with_low_variance.mkv	30.6 MB
03.05-identify_and_remove_rows_that_contain_duplicate_data.mkv	116.2 MB
03.06-defining_outliers.mkv	102.4 MB
03.07-remove_outliers-the_standard_deviation_approach.mkv	52.4 MB
03.08-remove_outliers-the_iqr_approach.mkv	42.7 MB
03.09-automatic_outlier_detection.mkv	52.7 MB
03.10-mark_missing_values.mkv	62.9 MB
03.11-remove_rows_with_missing_values.mkv	29.1 MB
03.12-statistical_imputation.mkv	6.3 MB
03.13-mean_value_imputation.mkv	43.9 MB
03.14-simple_imputer_with_model_evaluation.mkv	22.3 MB
03.15-compare_different_statistical_imputation_strategies.mkv	26.5 MB
03.16-k-nearest_neighbors_imputation.mkv	46.5 MB
03.17-knnimputer_and_model_evaluation.mkv	36.0 MB
03.18-iterative_imputation.mkv	39.4 MB
03.19-iterativeimputer_and_model_evaluation.mkv	19.3 MB
03.20-iterativeimputer_and_different_imputation_order.mkv	24.1 MB
/Section 4/
04.01-feature_selection_introduction.mkv	213.0 MB
04.02-feature_selection_defined.mkv	12.5 MB
04.03-statistics_for_feature_selection.mkv	109.4 MB
04.04-loading_a_categorical_dataset.mkv	29.0 MB
04.05-encode_the_dataset_for_modelling.mkv	26.2 MB
04.06-chi-squared.mkv	18.3 MB
04.07-mutual_information.mkv	19.1 MB
04.08-modeling_with_selected_categorical_features.mkv	39.2 MB
04.09-feature_selection_with_anova_on_numerical_input.mkv	43.8 MB
04.10-feature_selection_with_mutual_information.mkv	19.1 MB
04.11-modeling_with_selected_numerical_features.mkv	27.2 MB
04.12-tuning_a_number_of_selected_features.mkv	39.8 MB
04.13-select_features_for_numerical_output.mkv	23.8 MB
04.14-linear_correlation_with_correlation_statistics.mkv	27.5 MB
04.15-linear_correlation_with_mutual_information.mkv	30.8 MB
04.16-baseline_and_model_built_using_correlation.mkv	37.5 MB
04.17-model_built_using_mutual_information_features.mkv	12.0 MB
04.18-tuning_number_of_selected_features.mkv	57.3 MB
04.19-recursive_feature_elimination.mkv	185.1 MB
04.20-rfe_for_classification.mkv	53.5 MB
04.21-rfe_for_regression.mkv	27.5 MB
04.22-rfe_hyperparameters.mkv	34.2 MB
04.23-feature_ranking_for_rfe.mkv	31.0 MB
04.24-feature_importance_scores_defined.mkv	196.3 MB
04.25-feature_importance_scores_linear_regression.mkv	36.9 MB
04.26-feature_importance_scores_logistic_regression_and_cart.mkv	38.3 MB
04.27-feature_importance_scores_random_forests.mkv	17.8 MB
04.28-permutation_feature_importance.mkv	29.8 MB
04.29-feature_selection_with_importance.mkv	44.4 MB
/Section 5/
05.01-scale_numerical_data.mkv	11.6 MB
05.02-diabetes_dataset_for_scaling.mkv	24.2 MB
05.03-minmaxscaler_transform.mkv	25.4 MB
05.04-standardscaler_transform.mkv	29.9 MB
05.05-robust_scaling_data.mkv	44.6 MB
05.06-robust_scaler_applied_to_dataset.mkv	23.7 MB
05.07-explore_robust_scaler_range.mkv	15.6 MB
05.08-nominal_and_ordinal_variables.mkv	316.3 MB
05.09-ordinal_encoding.mkv	17.8 MB
05.10-one-hot_encoding_defined.mkv	3.9 MB
05.11-one-hot_encoding.mkv	18.1 MB
05.12-dummy_variable_encoding.mkv	18.3 MB
05.13-ordinal_encoder_transform_on_breast_cancer_dataset.mkv	47.9 MB
05.14-make_distributions_more_gaussian.mkv	9.3 MB
05.15-power_transform_on_contrived_dataset.mkv	22.4 MB
05.16-power_transform_on_sonar_dataset.mkv	30.4 MB
05.17-box-cox_on_sonar_dataset.mkv	33.3 MB
05.18-yeo-johnson_on_sonar_dataset.mkv	27.3 MB
05.19-polynomial_features.mkv	160.3 MB
05.20-effect_of_polynomial_degrees.mkv	20.2 MB
/Section 6/
06.01-transforming_different_data_types.mkv	24.6 MB
06.02-the_columntransformer.mkv	29.6 MB
06.03-the_columntransformer_on_abalone_dataset.mkv	37.0 MB
06.04-manually_transform_target_variable.mkv	25.7 MB
06.05-automatically_transform_target_variable.mkv	57.1 MB
06.06-challenge_of_preparing_new_data_for_a_model.mkv	258.9 MB
06.07-save_model_and_data_scaler.mkv	42.3 MB
06.08-load_and_apply_saved_scalers.mkv	18.8 MB
/Section 7/
07.01-curse_of_dimensionality.mkv	15.0 MB
07.02-techniques_for_dimensionality_reduction.mkv	102.2 MB
07.03-linear_discriminant_analysis.mkv	20.2 MB
07.04-linear_discriminant_analysis_demonstrated.mkv	51.5 MB
07.05-principal_component_analysis.mkv	62.7 MB
Total files 207