[FreeCoursesOnline.Me] PacktPub - Data Cleansing Master Class in Python

[FreeCoursesOnline.Me] PacktPub - Data Cleansing Master Class in Python

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01/07/22 at 9:01pm GMT+1
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Description
Lynda and other Courses >>> https://www.freecoursesonline.me/

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By: Mike West

Released: December 2021

Course Source: https://www.packtpub.com/product/data-cleansing-master-class-in-python-video/9781803239040



Video Details



ISBN 9781803239040

Course Length 3 hours 33 minutes



About



Data preparation may be the most important part of a machine learning project. It is the most time-consuming part, although it is the least discussed topic. Data preparation, sometimes referred to as data preprocessing, is the act of transforming raw data into a form that is appropriate for modeling.



Machine learning algorithms require input data to be numbered, and most algorithm implementations maintain this expectation. Therefore, if your data contains data types and values that are not numbers, such as labels, you will need to change the data into numbers. Further, specific machine learning algorithms have expectations regarding the data types, scale, probability distribution, and relationships between input variables, and you may need to change the data to meet these expectations.



In this course, you will learn data imputation and advanced data cleansing techniques, how to apply real-world data cleansing techniques to your data, advanced data cleansing techniques. Also, learn how to prepare data in a way that avoids data leakage, and in turn, incorrect model evaluation.



By the end of this course, you will perform data preprocessing and master data cleaning skills.



The complete code bundle for this course is available at https://github.com/PacktPublishing/Data-Cleansing-Master-Class-in-Python



Author



Mike West, is the founder of LogikBot. He has worked with databases for over two decades. He has worked for or consulted with over 50 different companies as a full-time employee or consultant. These were Fortune 500 as well as several small to mid-size companies. Some include Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light, and Northrup Grumman.



Over the last five years, Mike has transitioned to the exciting world of applied machine learning. He is excited to show you what he has learned and help you move into one of the single-most important fields in this space.






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File list
  • [FreeCoursesOnline.Me] PacktPub - Data Cleansing Master Class in Python
  • 0. Websites you may like/How you can help our Group!.txt 204 B
  • 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 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 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 MB
  • Section 3/03.11-remove_rows_with_missing_values.mkv 27.7 MB
  • Section 3/03.12-statistical_imputation.mkv 6 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 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 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 MB
  • Section 4/04.12-tuning_a_number_of_selected_features.mkv 38 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 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 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 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 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 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 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

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