├── .gitignore ├── LICENSE ├── README.md ├── abstract.rst ├── check_env.ipynb ├── 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 - Support Vector Machines.ipynb ├── 19 In Depth - Trees and Forests.ipynb ├── 20 Feature Selection.ipynb ├── 21 Unsupervised learning - Hierarchical and density-based clustering algorithms.ipynb ├── 22 Unsupervised learning - Non-linear dimensionality reduction.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 │ ├── 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 │ ├── 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 │ └── 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 │ ├── 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 │ ├── 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