gitextract_9qj13mdv/

├── .gitignore
├── LICENSE
├── README.md
├── abstract.rst
├── check_env.ipynb
├── fetch_data.py
├── 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
│   ├── figures/
│   │   ├── ML_flow_chart.py
│   │   ├── __init__.py
│   │   ├── plot_2d_separator.py
│   │   ├── plot_digits_dataset.py
│   │   ├── plot_helpers.py
│   │   ├── plot_interactive_forest.py
│   │   ├── plot_interactive_tree.py
│   │   ├── plot_kneighbors_regularization.py
│   │   ├── plot_linear_svc_regularization.py
│   │   ├── plot_pca.py
│   │   ├── plot_rbf_svm_parameters.py
│   │   └── plot_scaling.py
│   ├── helpers.py
│   └── 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
│       ├── 15A_ridge_grid.py
│       ├── 16A_avg_per_class_acc.py
│       ├── 17A_logreg_grid.py
│       ├── 17B_learning_curve_alpha.py
│       ├── 18_svc_grid.py
│       ├── 19_gbc_grid.py
│       ├── 20_univariate_vs_mb_selection.py
│       ├── 21_clustering_comparison.py
│       ├── 22A_isomap_digits.py
│       ├── 22B_tsne_classification.py
│       └── 23_batchtrain.py
├── requirements.txt
├── slides/
│   └── scipy2016.pptx
└── todo.rst