├── .gitignore ├── README.md ├── code ├── 00_python_beginner_workshop.py ├── 00_python_intermediate_workshop.py ├── 01_chipotle_homework_solution.py ├── 01_reading_files.py ├── 03_exploratory_analysis_pandas.py ├── 04_apis.py ├── 04_visualization.py ├── 05_iris_exercise.py ├── 05_sklearn_knn.py ├── 07_glass_id_homework_solution.py ├── 08_web_scraping.py ├── 10_logistic_regression_confusion_matrix.py ├── 13_naive_bayes.py ├── 15_kaggle.py ├── 17_ensembling_exercise.py ├── 18_clustering.py ├── 18_regularization.py ├── 19_advanced_sklearn.py ├── 19_gridsearchcv_exercise.py ├── 19_regex_exercise.py ├── 19_regex_reference.py ├── 20_sql.py └── 21_ensembles_example.py ├── data ├── SMSSpamCollection.txt ├── airline_safety.csv ├── auto_mpg.txt ├── chipotle_orders.tsv ├── default.csv ├── drinks.csv ├── homicides.txt ├── imdb_movie_ratings_top_1000.csv ├── imdb_movie_urls.csv ├── kaggle_tweets.csv ├── sales.db ├── titanic_train.csv ├── vehicles.db ├── vehicles_test.csv └── vehicles_train.csv ├── homework ├── 02_command_line_hw_soln.md ├── 03_pandas_hw_soln.py ├── 04_visualization_hw_soln.py ├── 06_bias_variance.md ├── 07_glass_identification.md ├── 11_roc_auc.md ├── 11_roc_auc_annotated.md ├── 13_spam_filtering.md └── 13_spam_filtering_annotated.md ├── notebooks ├── 06_bias_variance.ipynb ├── 06_model_evaluation_procedures.ipynb ├── 09_linear_regression.ipynb ├── 11_cross_validation.ipynb ├── 11_roc_auc.ipynb ├── 11_titanic_exercise.ipynb ├── 13_bayes_iris.ipynb ├── 13_naive_bayes_spam.ipynb ├── 14_nlp.ipynb ├── 16_decision_trees.ipynb ├── 17_ensembling.ipynb ├── 18_regularization.ipynb └── images │ ├── 18_bias_variance.png │ ├── 18_overfitting.png │ ├── 18_ridge_lasso_path.png │ ├── 18_ridge_lasso_regression_coefficients.png │ ├── 18_underfitting_overfitting.png │ ├── cross_validation_diagram.png │ ├── cross_validation_example.png │ ├── estimating_coefficients.png │ ├── obama_clinton_tree.jpg │ ├── overfitting.png │ ├── r_squared.png │ ├── salary_color.png │ ├── salary_regions.png │ ├── salary_tree.png │ ├── salary_tree_annotated.png │ ├── salary_unpruned.png │ ├── slope_intercept.png │ ├── train_test_split.png │ ├── training_error.png │ ├── tree_titanic.png │ ├── tree_vehicles.png │ ├── tree_vs_linear.png │ └── underfitting_overfitting.png ├── other ├── peer_review.md ├── project.md ├── public_data.md └── resources.md └── slides ├── 01_course_overview.pdf ├── 01_course_overview.pptx ├── 02_Introduction_to_the_Command_Line.md ├── 02_git_github.pdf ├── 02_git_github.pptx ├── 04_apis.pdf ├── 04_apis.pptx ├── 04_visualization.pdf ├── 04_visualization.pptx ├── 05_intro_to_data_science.pdf ├── 05_intro_to_data_science.pptx ├── 05_machine_learning_knn.pdf ├── 05_machine_learning_knn.pptx ├── 08_web_scraping.pdf ├── 08_web_scraping.pptx ├── 10_logistic_regression_confusion_matrix.pdf ├── 10_logistic_regression_confusion_matrix.pptx ├── 11_drawing_roc.pdf ├── 11_drawing_roc.pptx ├── 13_bayes_theorem.pdf ├── 13_bayes_theorem.pptx ├── 13_naive_bayes.pdf 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