首页 磁力链接怎么用

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

文件类型 收录时间 最后活跃 资源热度 文件大小 文件数量
视频 2022-5-23 02:09 2024-12-22 22:28 192 5.86 GB 103
二维码链接
[FreeCoursesOnline.Me] PacktPub - Data Cleansing Master Class in Python的二维码
种子下载(838888不存储任何种子文件)
种子下载线路1(迅雷)--推荐
种子下载线路2(比特彗星)
种子下载线路3(torcache)
3条线路均为国内外知名下载网站种子链接,内容跟本站无关!
文件列表
  1. Section 1/01.01-course_introduction.mkv152.85MB
  2. Section 1/01.02-course_structure.mkv157.17MB
  3. Section 1/01.03-is_this_course_right_for_you.mkv4.25MB
  4. Section 2/02.01-introducing_data_preparation.mkv277.14MB
  5. Section 2/02.02-the_machine_learning_process.mkv90.77MB
  6. Section 2/02.03-data_preparation_defined.mkv251.94MB
  7. Section 2/02.04-choosing_a_data_preparation_technique.mkv264.02MB
  8. Section 2/02.05-what_is_data_in_machine_learning.mkv75.71MB
  9. Section 2/02.06-raw_data.mkv115.35MB
  10. Section 2/02.07-machine_learning_is_mostly_data_preparation.mkv29.11MB
  11. Section 2/02.08-common_data_preparation_tasks-data_cleansing.mkv160.19MB
  12. Section 2/02.09-common_data_preparation_tasks-feature_selection.mkv51.66MB
  13. Section 2/02.10-common_data_preparation_tasks-data_transforms.mkv10.46MB
  14. Section 2/02.11-common_data_preparation_tasks-feature_engineering.mkv134.62MB
  15. Section 2/02.12-common_data_preparation_tasks-dimensionality_reduction.mkv9.14MB
  16. Section 2/02.13-data_leakage.mkv11.27MB
  17. Section 2/02.14-problem_with_naive_data_preparation.mkv142.92MB
  18. Section 2/02.15-case_study_data_leakage_train__test__split_naive_approach.mkv46.89MB
  19. Section 2/02.16-case_study_data_leakage_train__test__split_correct_approach.mkv27.23MB
  20. Section 2/02.17-case_study_data_leakage_k-fold_naive_approach.mkv39.56MB
  21. Section 2/02.18-case_study_data_leakage_k-fold_correct_approach.mkv35.4MB
  22. Section 3/03.01-data_cleansing_overview.mkv159.66MB
  23. Section 3/03.02-identify_columns_that_contain_a_single_value.mkv18.12MB
  24. Section 3/03.03-identify_columns_with_few_values.mkv31.19MB
  25. Section 3/03.04-remove_columns_with_low_variance.mkv29.14MB
  26. Section 3/03.05-identify_and_remove_rows_that_contain_duplicate_data.mkv110.79MB
  27. Section 3/03.06-defining_outliers.mkv97.67MB
  28. Section 3/03.07-remove_outliers-the_standard_deviation_approach.mkv50MB
  29. Section 3/03.08-remove_outliers-the_iqr_approach.mkv40.68MB
  30. Section 3/03.09-automatic_outlier_detection.mkv50.22MB
  31. Section 3/03.10-mark_missing_values.mkv60.03MB
  32. Section 3/03.11-remove_rows_with_missing_values.mkv27.74MB
  33. Section 3/03.12-statistical_imputation.mkv5.98MB
  34. Section 3/03.13-mean_value_imputation.mkv41.86MB
  35. Section 3/03.14-simple_imputer_with_model_evaluation.mkv21.26MB
  36. Section 3/03.15-compare_different_statistical_imputation_strategies.mkv25.32MB
  37. Section 3/03.16-k-nearest_neighbors_imputation.mkv44.39MB
  38. Section 3/03.17-knnimputer_and_model_evaluation.mkv34.33MB
  39. Section 3/03.18-iterative_imputation.mkv37.61MB
  40. Section 3/03.19-iterativeimputer_and_model_evaluation.mkv18.41MB
  41. Section 3/03.20-iterativeimputer_and_different_imputation_order.mkv23.03MB
  42. Section 4/04.01-feature_selection_introduction.mkv203.11MB
  43. Section 4/04.02-feature_selection_defined.mkv11.88MB
  44. Section 4/04.03-statistics_for_feature_selection.mkv104.3MB
  45. Section 4/04.04-loading_a_categorical_dataset.mkv27.65MB
  46. Section 4/04.05-encode_the_dataset_for_modelling.mkv25.02MB
  47. Section 4/04.06-chi-squared.mkv17.49MB
  48. Section 4/04.07-mutual_information.mkv18.2MB
  49. Section 4/04.08-modeling_with_selected_categorical_features.mkv37.43MB
  50. Section 4/04.09-feature_selection_with_anova_on_numerical_input.mkv41.78MB
  51. Section 4/04.10-feature_selection_with_mutual_information.mkv18.2MB
  52. Section 4/04.11-modeling_with_selected_numerical_features.mkv25.98MB
  53. Section 4/04.12-tuning_a_number_of_selected_features.mkv37.97MB
  54. Section 4/04.13-select_features_for_numerical_output.mkv22.68MB
  55. Section 4/04.14-linear_correlation_with_correlation_statistics.mkv26.18MB
  56. Section 4/04.15-linear_correlation_with_mutual_information.mkv29.38MB
  57. Section 4/04.16-baseline_and_model_built_using_correlation.mkv35.73MB
  58. Section 4/04.17-model_built_using_mutual_information_features.mkv11.42MB
  59. Section 4/04.18-tuning_number_of_selected_features.mkv54.69MB
  60. Section 4/04.19-recursive_feature_elimination.mkv176.57MB
  61. Section 4/04.20-rfe_for_classification.mkv51.03MB
  62. Section 4/04.21-rfe_for_regression.mkv26.21MB
  63. Section 4/04.22-rfe_hyperparameters.mkv32.64MB
  64. Section 4/04.23-feature_ranking_for_rfe.mkv29.59MB
  65. Section 4/04.24-feature_importance_scores_defined.mkv187.17MB
  66. Section 4/04.25-feature_importance_scores_linear_regression.mkv35.15MB
  67. Section 4/04.26-feature_importance_scores_logistic_regression_and_cart.mkv36.53MB
  68. Section 4/04.27-feature_importance_scores_random_forests.mkv17.01MB
  69. Section 4/04.28-permutation_feature_importance.mkv28.41MB
  70. Section 4/04.29-feature_selection_with_importance.mkv42.35MB
  71. Section 5/05.01-scale_numerical_data.mkv11.06MB
  72. Section 5/05.02-diabetes_dataset_for_scaling.mkv23.04MB
  73. Section 5/05.03-minmaxscaler_transform.mkv24.25MB
  74. Section 5/05.04-standardscaler_transform.mkv28.5MB
  75. Section 5/05.05-robust_scaling_data.mkv42.49MB
  76. Section 5/05.06-robust_scaler_applied_to_dataset.mkv22.6MB
  77. Section 5/05.07-explore_robust_scaler_range.mkv14.91MB
  78. Section 5/05.08-nominal_and_ordinal_variables.mkv301.65MB
  79. Section 5/05.09-ordinal_encoding.mkv17.01MB
  80. Section 5/05.10-one-hot_encoding_defined.mkv3.7MB
  81. Section 5/05.11-one-hot_encoding.mkv17.27MB
  82. Section 5/05.12-dummy_variable_encoding.mkv17.46MB
  83. Section 5/05.13-ordinal_encoder_transform_on_breast_cancer_dataset.mkv45.66MB
  84. Section 5/05.14-make_distributions_more_gaussian.mkv8.88MB
  85. Section 5/05.15-power_transform_on_contrived_dataset.mkv21.34MB
  86. Section 5/05.16-power_transform_on_sonar_dataset.mkv28.99MB
  87. Section 5/05.17-box-cox_on_sonar_dataset.mkv31.78MB
  88. Section 5/05.18-yeo-johnson_on_sonar_dataset.mkv26.03MB
  89. Section 5/05.19-polynomial_features.mkv152.87MB
  90. Section 5/05.20-effect_of_polynomial_degrees.mkv19.25MB
  91. Section 6/06.01-transforming_different_data_types.mkv23.42MB
  92. Section 6/06.02-the_columntransformer.mkv28.22MB
  93. Section 6/06.03-the_columntransformer_on_abalone_dataset.mkv35.33MB
  94. Section 6/06.04-manually_transform_target_variable.mkv24.53MB
  95. Section 6/06.05-automatically_transform_target_variable.mkv54.43MB
  96. Section 6/06.06-challenge_of_preparing_new_data_for_a_model.mkv246.91MB
  97. Section 6/06.07-save_model_and_data_scaler.mkv40.38MB
  98. Section 6/06.08-load_and_apply_saved_scalers.mkv17.94MB
  99. Section 7/07.01-curse_of_dimensionality.mkv14.33MB
  100. Section 7/07.02-techniques_for_dimensionality_reduction.mkv97.49MB
  101. Section 7/07.03-linear_discriminant_analysis.mkv19.26MB
  102. Section 7/07.04-linear_discriminant_analysis_demonstrated.mkv49.11MB
  103. Section 7/07.05-principal_component_analysis.mkv59.75MB
友情提示
不会用的朋友看这里 把磁力链接复制到离线下载,或者bt下载软件里即可下载文件,或者直接复制迅雷链接到迅雷里下载! 亲,你造吗?将网页分享给您的基友,下载的人越多速度越快哦!

违规内容投诉邮箱:[email protected]

概述 838888磁力搜索是一个磁力链接搜索引擎,是学术研究的副产品,用于解决资源过度分散的问题 它通过BitTorrent协议加入DHT网络,实时的自动采集数据,仅存储文件的标题、大小、文件列表、文件标识符(磁力链接)等基础信息 838888磁力搜索不下载任何真实资源,无法判断资源的合法性及真实性,使用838888磁力搜索服务的用户需自行鉴别内容的真伪 838888磁力搜索不上传任何资源,不提供Tracker服务,不提供种子文件的下载,这意味着838888磁力搜索 838888磁力搜索是一个完全合法的系统