Packt Data Science Projects with Python-XQZT

Another exquisit release
Packt.Data.Science.Projects.with.Python-XQZT
     Title: Data Science Projects with Python
 Publisher: Packt
  Category: Data
      Size: 7225M
     Files: 12F
      Date: 2021-06-01
  Course #: 9781838986063
 Published: Packt
   Updated: N/A
       URL: https://www.packtpub.com/video/data/9781838986063
    Author: Barbora stetinova
  Duration: 6 hours 7 minutes
 Exer/Code: [X]
Installation:
Unpack that shit, run that shit
Description:
Use pandas and Matplotlib to critically examine a dataset with
summary statistics and graphs and extract meaningful

52M	01.01-course_overview.mkv
99M	01.02-installation_and_setup.mkv
59M	01.03-lesson_overview.mkv
302M	01.04-python_and_the_anaconda_package_management_system.mkv
79M	01.05-different_types_of_data_science_problems.mkv
139M	01.06-loading_the_case_study_data_with_jupyter_and_pandas.mkv
183M	01.07-getting_familiar_with_data_and_performing_data_cleaning.mkv
173M	01.08-boolean_masks.mkv
198M	01.09-data_quality_assurance_and_exploration.mkv
110M	01.10-deep_dive_categorical_features.mkv
119M	01.11-exploring_the_financial_history_features_in_the_dataset.mkv
50M	01.12-lesson_summary.mkv
60M	02.01-lesson_overview.mkv
50M	02.02-exploring_the_response_variable_and_concluding_the_initial_exploration.mkv
164M	02.03-introduction_to_scikit-learn.mkv
157M	02.04-model_performance_metrics_for_binary_classification.mkv
166M	02.05-true_positive_rate_false_positive_rate_and_confusion_matrix.mkv
135M	02.06-obtaining_predicted_probabilities_from_a_trained_logistic_regression_model.mkv
5.0M	02.07-lesson_summary.mkv
46M	03.01-lesson_overview.mkv
249M	03.02-examining_the_relationships_between_features_and_the_response.mkv
199M	03.03-finer_points_of_the_f-test_equivalence_to_t-test_for_two_classes_and_cautions.mkv
301M	03.04-univariate_feature_selection_what_it_does_and_doesnt_do.mkv
246M	03.05-generalized_linear_models_(glms).mkv
6.0M	03.06-lesson_summary.mkv
61M	04.01-lesson_overview.mkv
188M	04.02-estimating_the_coefficients_and_intercepts_of_logistic_regression.mkv
151M	04.03-assumptions_of_logistic_regression.mkv
262M	04.04-how_many_features_should_you_include.mkv
321M	04.05-lasso_(l1)_and_ridge_(l2)_regularization.mkv
101M	04.06-cross_validation_choosing_the_regularization_parameter_and_other_hyperparameters.mkv
216M	04.07-reducing_overfitting_on_the_synthetic_data_classification_problem.mkv
146M	04.08-options_for_logistic_regression_in_scikit-learn.mkv
9.8M	04.09-lesson_summary.mkv
42M	05.01-lesson_overview.mkv
386M	05.02-decision_trees.mkv
241M	05.03-training_decision_trees_node_impurity.mkv
206M	05.04-using_decision_trees_advantages_and_predicted_probabilities.mkv
199M	05.05-random_forests_ensembles_of_decision_trees.mkv
111M	05.06-fitting_a_random_forest.mkv
6.7M	05.07-lesson_summary.mkv
67M	06.01-lesson_overview.mkv
88M	06.02-review_of_modeling_results.mkv
184M	06.03-dealing_with_missing_data_imputation_strategies.mkv
106M	06.04-cleaning_the_dataset.mkv
140M	06.05-mode_and_random_imputation_of_pay_1.mkv
116M	06.06-a_predictive_model_for_pay_1.mkv
142M	06.07-using_the_imputation_model_and_comparing_it_to_other_methods.mkv
244M	06.08-financial_analysis.mkv
114M	06.09-final_thoughts_on_delivering_the_predictive_model_to_the_client.mkv
10M	06.10-lesson_summary.mkv
23M	9781838986063_Code.zip
7.1G	total