【文件属性】：
文件名称：Machine learning mastery with python (book + complete code for 20 projects)
文件大小：1.89MB
文件格式：ZIP
更新时间：2021-03-04 08:19:39
Machine learning, Deep learning, Python
Welcome to Machine Learning Mastery With Python. This book is your guide to applied machine learning with Python. You will discover the step-by-step process that you can use to get started and become good at machine learning for predictive modeling with the Python ecosystem.
The book with help you understand your data, create accurate models and build projects for end-to-end.
【文件预览】：
README.txt
machine_learning_mastery_with_python.pdf
code
----chapter_10()
--------pima-indians-diabetes.data.csv(23KB)
--------regression_mae.py(702B)
--------classification_confusion_matrix.py(729B)
--------classification_report.py(729B)
--------housing.csv(48KB)
--------classification_auc.py(659B)
--------classification_logloss.py(668B)
--------classification_accuracy.py(666B)
--------regression_mse.py(716B)
--------regression_rsquared.py(681B)
----chapter_18()
--------project_template.py(599B)
----chapter_06()
--------pima-indians-diabetes.data.csv(23KB)
--------boxplot.py(339B)
--------density_plots.py(332B)
--------correlation_matrix.py(564B)
--------correlation_matrix_generic.py(439B)
--------histograms.py(272B)
--------scatterplot_matrix.py(321B)
----chapter_20()
--------housing.csv(48KB)
--------project_regression_boston.py(7KB)
----chapter_19()
--------iris.data.csv(4KB)
--------project_classification_iris.py(3KB)
----chapter_17()
--------pima-indians-diabetes.data.csv(23KB)
--------save_model_joblib.py(857B)
--------save_model_pickel.py(845B)
----chapter_16()
--------pima-indians-diabetes.data.csv(23KB)
--------random_search.py(652B)
--------grid_search.py(621B)
----chapter_21()
--------project_classification_sonar.py(7KB)
--------sonar.all-data.csv(86KB)
----chapter_13()
--------pima-indians-diabetes.data.csv(23KB)
--------race_algorithms.py(1KB)
----chapter_11()
--------support_vector_machines_classification.py(525B)
--------pima-indians-diabetes.data.csv(23KB)
--------logistic_regression.py(595B)
--------linear_discriminant_analysis.py(604B)
--------classification_and_regression_trees_classification.py(565B)
--------k_nearest_neighbors_classification.py(580B)
--------gaussian_naive_bayes.py(564B)
----chapter_07()
--------rescale_data.py(582B)
--------normalize_data.py(563B)
--------pima-indians-diabetes.data.csv(23KB)
--------binarization.py(556B)
--------standardize_data.py(573B)
----chapter_09()
--------loocv.py(634B)
--------pima-indians-diabetes.data.csv(23KB)
--------cross_validation.py(670B)
--------shuffle_split.py(733B)
--------train_test.py(659B)
----chapter_03()
--------python_crash_course.py(1KB)
--------pandas_crash_course.py(552B)
--------numpy_crash_course.py(621B)
--------matplotlib_crash_course.py(407B)
----chapter_14()
--------pima-indians-diabetes.data.csv(23KB)
--------standardize_model_pipeline.py(891B)
--------feature_union_model_pipeline.py(1KB)
----chapter_08()
--------pima-indians-diabetes.data.csv(23KB)
--------recursive_feature_elimination.py(614B)
--------univariate_selection.py(715B)
--------pca.py(508B)
--------feature_importance.py(467B)
----chapter_05()
--------pima-indians-diabetes.data.csv(23KB)
--------skew.py(249B)
--------describe.py(353B)
--------pearson_correlation.py(377B)
--------head.py(246B)
--------class_distribution.py(277B)
--------dimensions.py(250B)
--------data_types.py(257B)
----chapter_02()
--------scipy_versions.py(263B)
--------sklearn_version.py(72B)
----chapter_15()
--------pima-indians-diabetes.data.csv(23KB)
--------voting_ensemble_classification.py(974B)
--------bagged_cart_classification.py(750B)
--------gradient_boosting_classification.py(670B)
--------extra_trees_classification.py(654B)
--------adaboost_classification.py(630B)
--------random_forest_classification.py(660B)
----chapter_12()
--------elastic_net.py(642B)
--------housing.csv(48KB)
--------lasso_regression.py(627B)
--------k_nearest_neighbors_regression.py(650B)
--------support_vector_machines_regression.py(612B)
--------ridge_regression.py(627B)
--------classification_and_regression_trees_regression.py(659B)
--------linear_regression.py(650B)
----chapter_04()
--------pima-indians-diabetes.data.csv(23KB)
--------load_csv_np_url.py(211B)
--------load_csv_np.py(183B)
--------load_csv.py(283B)
--------load_csv_pandas_url.py(224B)
--------load_csv_pandas.py(268B)