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RAR-files |
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| packt.data.science.projects.with.python-xqzt.r00 |
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| packt.data.science.projects.with.python-xqzt.r01 |
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| packt.data.science.projects.with.python-xqzt.r02 |
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| packt.data.science.projects.with.python-xqzt.r03 |
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| packt.data.science.projects.with.python-xqzt.r04 |
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| Total size: |
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|
| Archived
files |
|
01.01-course_overview.mkv
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53,679,417 |
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01.02-installation_and_setup.mkv
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103,030,812 |
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01.03-lesson_overview.mkv
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01.04-python_and_the_anaconda_package_management_system.mkv
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316,411,082 |
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01.05-different_types_of_data_science_problems.mkv
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01.06-loading_the_case_study_data_with_jupyter_and_pandas.mkv
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145,675,962 |
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01.07-getting_familiar_with_data_and_performing_data_cleaning.mkv
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191,526,871 |
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01.08-boolean_masks.mkv
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180,828,108 |
14B675E2 |
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01.09-data_quality_assurance_and_exploration.mkv
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207,368,866 |
AEA4612A |
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01.10-deep_dive_categorical_features.mkv
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114,712,671 |
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01.11-exploring_the_financial_history_features_in_the_dataset.mkv
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124,473,434 |
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01.12-lesson_summary.mkv
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51,568,537 |
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02.01-lesson_overview.mkv
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62,318,373 |
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02.02-exploring_the_response_variable_and_concluding_the_initial_exploration.mkv
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51,737,937 |
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02.03-introduction_to_scikit-learn.mkv
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02.04-model_performance_metrics_for_binary_classification.mkv
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02.05-true_positive_rate_false_positive_rate_and_confusion_matrix.mkv
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02.06-obtaining_predicted_probabilities_from_a_trained_logistic_regression_model.mkv
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02.07-lesson_summary.mkv
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03.01-lesson_overview.mkv
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03.02-examining_the_relationships_between_features_and_the_response.mkv
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03.03-finer_points_of_the_f-test_equivalence_to_t-test_for_two_classes_and_cautions.mkv
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208,135,729 |
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03.04-univariate_feature_selection_what_it_does_and_doesnt_do.mkv
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314,679,453 |
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03.05-generalized_linear_models_(glms).mkv
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257,661,030 |
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03.06-lesson_summary.mkv
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04.01-lesson_overview.mkv
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63,069,676 |
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04.02-estimating_the_coefficients_and_intercepts_of_logistic_regression.mkv
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196,870,815 |
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04.03-assumptions_of_logistic_regression.mkv
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158,065,816 |
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04.04-how_many_features_should_you_include.mkv
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274,447,051 |
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04.05-lasso_(l1)_and_ridge_(l2)_regularization.mkv
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335,857,403 |
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04.06-cross_validation_choosing_the_regularization_parameter_and_other_hyperparameters.mkv
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105,865,993 |
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04.07-reducing_overfitting_on_the_synthetic_data_classification_problem.mkv
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226,448,374 |
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04.08-options_for_logistic_regression_in_scikit-learn.mkv
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152,741,722 |
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04.09-lesson_summary.mkv
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10,172,203 |
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05.01-lesson_overview.mkv
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43,671,638 |
227FA2AD |
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05.02-decision_trees.mkv
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404,741,043 |
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05.03-training_decision_trees_node_impurity.mkv
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252,490,538 |
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05.04-using_decision_trees_advantages_and_predicted_probabilities.mkv
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215,328,289 |
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05.05-random_forests_ensembles_of_decision_trees.mkv
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208,383,974 |
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05.06-fitting_a_random_forest.mkv
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115,449,452 |
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05.07-lesson_summary.mkv
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6,987,466 |
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06.01-lesson_overview.mkv
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69,260,229 |
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06.02-review_of_modeling_results.mkv
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91,478,160 |
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06.03-dealing_with_missing_data_imputation_strategies.mkv
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192,670,610 |
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06.04-cleaning_the_dataset.mkv
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110,479,379 |
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06.05-mode_and_random_imputation_of_pay_1.mkv
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146,233,671 |
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06.06-a_predictive_model_for_pay_1.mkv
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120,771,772 |
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06.07-using_the_imputation_model_and_comparing_it_to_other_methods.mkv
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148,684,173 |
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06.08-financial_analysis.mkv
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254,926,205 |
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06.09-final_thoughts_on_delivering_the_predictive_model_to_the_client.mkv
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119,266,558 |
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06.10-lesson_summary.mkv
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10,412,367 |
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9781838986063_Code.zip |
23,260,935 |
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| Total size: |
7,555,484,308 |
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