├── .gitattributes ├── README.md ├── code ├── homework_answers │ ├── homework1.py │ ├── homework2.py │ └── homework3.py ├── section_ii │ ├── k_means_two_variables.py │ ├── k_means_two_variables_hc.kt │ ├── k_means_two_variables_hc.py │ ├── k_means_two_variables_numpy_hc.py │ ├── k_means_two_variables_sklearn.py │ ├── linear_regression_hc.py │ ├── linear_regression_numpy_hc.py │ ├── linear_regression_sklearn.py │ ├── multiple_linear_regression_hc.py │ ├── quadratic_quantile_regression.py │ ├── quadratic_regression_hc.py │ ├── quantile_regression_hc.py │ └── square_root_hc.py ├── section_iii │ ├── employee_retention_logistic_regression.py │ ├── employee_retention_logistic_regression_numpy.py │ ├── employee_retention_logistic_regression_sklearn.py │ ├── light_dark_font_logistic_regression.kt │ ├── light_dark_font_logistic_regression.py │ ├── normal_distribution_hc.py │ ├── simple_logistic_regression_hc.py │ ├── simple_logistic_regression_hc_numpy.py │ └── simple_logistic_regression_sklearn.py ├── section_iv │ ├── naive_bayes_bank_transactions.py │ └── naive_bayes_email_spam.py ├── section_v │ ├── employment_retention_decision_tree.py │ ├── employment_retention_random_forest.py │ ├── good_weather_decision_tree.kt │ ├── good_weather_decision_tree.py │ ├── good_weather_random_forest.py │ ├── light_dark_font_decision_tree.py │ ├── tree.png │ └── weather.xlsx └── section_vi │ ├── light_dark_font_neural_network_backpropagation.py │ ├── light_dark_font_neural_network_hill_climbing.py │ └── light_dark_font_neural_network_simulated_annealing.py ├── exercises ├── exercise1.py ├── exercise1_answer.py ├── exercise2.py ├── exercise2_answer.py ├── exercise3.py ├── exercise3_answer.py ├── exercise4.py └── exercise4_answer.py └── oreilly_machine_learning_from_scratch.pptx /.gitattributes: -------------------------------------------------------------------------------- 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