【文件属性】：
文件名称：Machine Learning Mastery - Machine Learning with Python
文件大小：1.9MB
文件格式：ZIP
更新时间：2022-09-21 05:30:09
Machine Learning Python Scikit-Learn
https://machinelearningmastery.com/machine-learning-with-python/
书和代码都有
【文件预览】：
machine_learning_mastery_with_python.pdf
code
----chapter_17()
--------save_model_joblib.py(875B)
--------save_model_pickle.py(863B)
--------pima-indians-diabetes.data.csv(23KB)
----chapter_21()
--------project_classification_sonar.py(7KB)
--------sonar.all-data.csv(86KB)
----chapter_03()
--------pandas_crash_course.py(552B)
--------numpy_crash_course.py(621B)
--------matplotlib_crash_course.py(407B)
--------python_crash_course.py(1KB)
----chapter_19()
--------project_classification_iris.py(3KB)
--------iris.data.csv(4KB)
----chapter_13()
--------race_algorithms.py(2KB)
--------pima-indians-diabetes.data.csv(23KB)
----chapter_16()
--------pima-indians-diabetes.data.csv(23KB)
--------grid_search.py(647B)
--------random_search.py(678B)
----chapter_08()
--------feature_importance.py(483B)
--------pca.py(508B)
--------univariate_selection.py(715B)
--------pima-indians-diabetes.data.csv(23KB)
--------recursive_feature_elimination.py(632B)
----chapter_18()
--------project_template.py(599B)
----chapter_05()
--------dimensions.py(250B)
--------class_distribution.py(277B)
--------skew.py(249B)
--------pearson_correlation.py(377B)
--------pima-indians-diabetes.data.csv(23KB)
--------describe.py(353B)
--------data_types.py(257B)
--------head.py(246B)
----chapter_07()
--------binarization.py(556B)
--------standardize_data.py(573B)
--------rescale_data.py(582B)
--------normalize_data.py(563B)
--------pima-indians-diabetes.data.csv(23KB)
----chapter_14()
--------feature_union_model_pipeline.py(1KB)
--------pima-indians-diabetes.data.csv(23KB)
--------standardize_model_pipeline.py(891B)
----chapter_10()
--------classification_confusion_matrix.py(747B)
--------classification_auc.py(677B)
--------regression_rsquared.py(681B)
--------pima-indians-diabetes.data.csv(23KB)
--------classification_accuracy.py(684B)
--------classification_report.py(747B)
--------classification_logloss.py(686B)
--------regression_mae.py(702B)
--------housing.csv(48KB)
--------regression_mse.py(701B)
----chapter_04()
--------load_csv_pandas.py(268B)
--------pima-indians-diabetes.data.csv(23KB)
--------load_csv_np.py(183B)
--------load_csv_pandas_url.py(224B)
--------load_csv.py(283B)
--------load_csv_np_url.py(211B)
----chapter_15()
--------adaboost_classification.py(630B)
--------gradient_boosting_classification.py(670B)
--------random_forest_classification.py(660B)
--------extra_trees_classification.py(654B)
--------voting_ensemble_classification.py(1004B)
--------pima-indians-diabetes.data.csv(23KB)
--------bagged_cart_classification.py(750B)
----chapter_06()
--------boxplot.py(339B)
--------scatterplot_matrix.py(321B)
--------histograms.py(272B)
--------pima-indians-diabetes.data.csv(23KB)
--------correlation_matrix.py(564B)
--------correlation_matrix_generic.py(439B)
--------density_plots.py(332B)
----chapter_02()
--------sklearn_version.py(72B)
--------scipy_versions.py(263B)
----chapter_12()
--------classification_and_regression_trees_regression.py(659B)
--------ridge_regression.py(627B)
--------elastic_net.py(642B)
--------linear_regression.py(650B)
--------support_vector_machines_regression.py(624B)
--------k_nearest_neighbors_regression.py(650B)
--------lasso_regression.py(627B)
--------housing.csv(48KB)
----chapter_11()
--------logistic_regression.py(598B)
--------support_vector_machines_classification.py(537B)
--------gaussian_naive_bayes.py(564B)
--------k_nearest_neighbors_classification.py(565B)
--------pima-indians-diabetes.data.csv(23KB)
--------classification_and_regression_trees_classification.py(565B)
--------linear_discriminant_analysis.py(589B)
----chapter_20()
--------project_regression_boston.py(7KB)
--------housing.csv(48KB)
----chapter_09()
--------cross_validation.py(654B)
--------loocv.py(652B)
--------train_test.py(677B)
--------pima-indians-diabetes.data.csv(23KB)
--------shuffle_split.py(751B)
README.txt