├── .gitignore ├── .travis.yml ├── LICENSE ├── README.md ├── abstract.rst ├── check_env.ipynb ├── environment.yml ├── fetch_data.py ├── images ├── check_env-1.png ├── check_env-2.png └── download-repo.png ├── notebooks ├── 01.Introduction_to_Machine_Learning.ipynb ├── 02.Scientific_Computing_Tools_in_Python.ipynb ├── 03.Data_Representation_for_Machine_Learning.ipynb ├── 04.Training_and_Testing_Data.ipynb ├── 05.Supervised_Learning-Classification.ipynb ├── 06.Supervised_Learning-Regression.ipynb ├── 07.Unsupervised_Learning-Transformations_and_Dimensionality_Reduction.ipynb ├── 08.Unsupervised_Learning-Clustering.ipynb ├── 09.Review_of_Scikit-learn_API.ipynb ├── 10.Case_Study-Titanic_Survival.ipynb ├── 11.Text_Feature_Extraction.ipynb ├── 12.Case_Study-SMS_Spam_Detection.ipynb ├── 13.Cross_Validation.ipynb ├── 14.Model_Complexity_and_GridSearchCV.ipynb ├── 15.Pipelining_Estimators.ipynb ├── 16.Performance_metrics_and_Model_Evaluation.ipynb ├── 17.In_Depth-Linear_Models.ipynb ├── 18.In_Depth-Trees_and_Forests.ipynb ├── 19.Feature_Selection.ipynb ├── 20.Unsupervised_learning-Hierarchical_and_density-based_clustering_algorithms.ipynb ├── 21.Unsupervised_learning-Non-linear_dimensionality_reduction.ipynb ├── 22.Unsupervised_learning-anomaly_detection.ipynb ├── 23.Out-of-core_Learning_Large_Scale_Text_Classification.ipynb ├── datasets │ ├── smsspam │ │ ├── SMSSpamCollection │ │ └── readme │ └── titanic3.csv ├── figures │ ├── ML_flow_chart.py │ ├── __init__.py │ ├── average-per-class.png │ ├── bag_of_words.svg │ ├── check_env-1.png │ ├── cluster_comparison.png │ ├── clustering-linkage.png │ ├── clustering.png │ ├── cross_validation.svg │ ├── data_representation.svg │ ├── dbscan.png │ ├── feature_union.svg │ ├── grid_search_cross_validation.svg │ ├── hashing_vectorizer.svg │ ├── ipython_help-1.png │ ├── ipython_help-2.png │ ├── ipython_run_cell.png │ ├── iris_setosa.jpg │ ├── iris_versicolor.jpg │ ├── iris_virginica.jpg │ ├── ml_taxonomy.png │ ├── overfitting_underfitting_cartoon.svg │ ├── petal_sepal.jpg │ ├── pipeline.svg │ ├── pipeline_cross_validation.svg │ ├── plot_2d_separator.py │ ├── plot_digits_dataset.py │ ├── plot_helpers.py │ ├── plot_interactive_forest.py │ ├── plot_interactive_tree.py │ ├── plot_kneigbors_regularization.png │ ├── plot_kneighbors_regularization.py │ ├── plot_linear_svc_regularization.py │ ├── plot_pca.py │ ├── plot_rbf_svm_parameters.py │ ├── plot_scaling.py │ ├── randomized_search.png │ ├── supervised_scikit_learn.png │ ├── supervised_workflow.svg │ ├── train_test_split.svg │ ├── train_test_split_matrix.svg │ ├── train_validation_test2.svg │ ├── tree_plotting.py │ └── unsupervised_workflow.svg ├── helpers.py ├── images │ ├── parallel_text_clf.png │ └── parallel_text_clf_average.png └── solutions │ ├── 03A_faces_plot.py │ ├── 04_wrong-predictions.py │ ├── 05A_knn_with_diff_k.py │ ├── 06A_knn_vs_linreg.py │ ├── 06B_lin_with_sine.py │ ├── 07A_iris-pca.py │ ├── 08B_digits_clustering.py │ ├── 10_titanic.py │ ├── 11_ngrams.py │ ├── 12A_tfidf.py │ ├── 12B_vectorizer_params.py │ ├── 13_cross_validation.py │ ├── 14_grid_search.py │ ├── 15A_ridge_grid.py │ ├── 16A_avg_per_class_acc.py │ ├── 17A_logreg_grid.py │ ├── 17B_learning_curve_alpha.py │ ├── 18_gbc_grid.py │ ├── 19_univariate_vs_mb_selection.py │ ├── 20_clustering_comparison.py │ ├── 21A_isomap_digits.py │ ├── 21B_tsne_classification.py │ ├── 22_A-anomaly_ocsvm_gamma.py │ ├── 22_B-anomaly_iforest_n_trees.py │ ├── 22_C-anomaly_digits.py │ └── 23_batchtrain.py ├── requirements.txt └── todo.rst /.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/amueller/scipy-2018-sklearn/HEAD/.gitignore -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/amueller/scipy-2018-sklearn/HEAD/.travis.yml 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