├── .gitignore ├── Makefile ├── README.md ├── fetch_data.py ├── ipynbhelper.py ├── notebooks ├── 01_introduction.ipynb ├── 02_representation_of_data.ipynb ├── 03_basic_principles_of_machine_learning.ipynb ├── 04_pandas_and_heterogeneous_data_modeling.ipynb ├── 05.1_supervised_classification.ipynb ├── 05.2_supervised_regression.ipynb ├── 05.3_measuring_prediction_performance.ipynb ├── 06_application_to_face_recognition.ipynb ├── 07.1_parameter_selection_validation_and_testing.ipynb ├── 07.2_cross_validation_and_model_selection.ipynb ├── 08_unsupervised_dimreduction.ipynb ├── figures │ ├── ML_flow_chart.py │ ├── __init__.py │ ├── bias_variance.py │ ├── grid_search_cv_splits.png │ ├── grid_search_parameters.png │ ├── iris_setosa.jpg │ ├── iris_versicolor.jpg │ ├── iris_virginica.jpg │ ├── linear_regression.py │ ├── sgd_separator.py │ ├── supervised_scikit_learn.png │ └── svm_gui_frames.py ├── helpers.py ├── images │ ├── parallel_text_clf.png │ ├── parallel_text_clf_average.png │ └── predictive_modeling_data_flow.png ├── solutions │ ├── 02A_faces_plot.py │ ├── 04A_plot_logistic_regression_weights.py │ ├── 04B_more_categorical_variables.py │ ├── 04C_feature_importance.py │ ├── 05B_houses_plot.py │ ├── 05B_houses_regression.py │ ├── 05C_validation_exercise.py │ ├── 07B_basic_grid_search.py │ ├── 07B_learning_curves.py │ └── 08A_digits_projection.py └── titanic_train.csv ├── rendered_notebooks ├── 01_introduction.ipynb ├── 02_representation_of_data.ipynb ├── 03_basic_principles_of_machine_learning.ipynb ├── 04_pandas_and_heterogenous_data_modeling.ipynb ├── 05.1_supervised_classification.ipynb ├── 05.2_supervised_regression.ipynb ├── 05.3_measuring_prediction_performance.ipynb ├── 06_application_to_face_recognition.ipynb ├── 07.1_validation_and_testing.ipynb ├── 07.2_validation_exercise.ipynb └── 08.1_unsupervised_dimreduction.ipynb └── svm_gui.py /.gitignore: -------------------------------------------------------------------------------- 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