├── README.md ├── chapter_1 ├── .DS_Store ├── 1_01_order_of_operations.py ├── 1_02_order_of_operations.py ├── 1_03_declare_variable.py ├── 1_04_greek_symbols.py ├── 1_05_subscripted_variables.py ├── 1_06_linear_function.py ├── 1_07_linear_function_sympy.py ├── 1_08_exponential_function.py ├── 1_09_03d_function.py ├── 1_10_summations.py ├── 1_11_summations_elements.py ├── 1_12_log_function.py ├── 1_13_interest.py ├── 1_14_continuous_interest.py ├── 1_15_natural_log_10.py ├── 1_16_02_limits_eulers_number.py ├── 1_16_limits_sympy.py ├── 1_17_derivative_function.py ├── 1_18_derivative_sympy.py ├── 1_19_derivative_python.py ├── 1_20_deriviatve_subst.py ├── 1_21_partial_derivatives_sympy.py ├── 1_22_limits_calculate_slopes.py ├── 1_23_limits_calculate_derivative.py ├── 1_24_chain_rule_sympy.py ├── 1_25_chain_rule_proof.py ├── 1_26_integral_approximation.py ├── 1_27_integral_ex1.py ├── 1_27_integral_ex2.py ├── 1_29_sympy_integration.py └── 1_30_limits_integrals_sympy.py ├── chapter_2 ├── .DS_Store ├── 2_01_bayes_theorem.py ├── 2_02_scipy_binomial.py ├── 2_03_scipy_beta.py ├── 2_04_scipy_beta_subtraction.py ├── 2_05_scipy_beta_more_trials.py └── 2_06_scipy_beta_middle_area.py ├── chapter_3 ├── 3_01_mean_in_python.py ├── 3_02_weighted_mean.py ├── 3_03_weighted_mean_ex2.py ├── 3_04_median_python.py ├── 3_05_mode_python.py ├── 3_06_variance_python.py ├── 3_07_standard_deviation_python.py ├── 3_08_standard_deviation_sample.py ├── 3_09_normal_distribution.py ├── 3_10_normal_cdf.py ├── 3_11_normal_cdf_middle.py ├── 3_12_inverse_cdf.py ├── 3_13_normal_random_number_generation.py ├── 3_14_z_scores.py ├── 3_15_central_limit_theorem.py ├── 3_16_critical_z_value.py ├── 3_17_confidence_interval.py ├── 3_18_cold_recovery_time.py ├── 3_19_area_one_tail.py ├── 3_20_one_tailed_pvalue.py ├── 3_21_area_two_tail.py ├── 3_22_two_tail_pvalue.py └── 3_23_t_dist_critical_range.py ├── chapter_4 ├── .DS_Store ├── 4_01_vector.py ├── 4_02_vector_numpy.py ├── 4_03_03d_vector.py ├── 4_04_05d_vector.py ├── 4_05_vector_addition.py ├── 4_06_vector_scaling.py ├── 4_07_matrix_vector_multiplication.py ├── 4_08_basis_vector_multiplication.py ├── 4_09_transforming_vector.py ├── 4_10_complicated_transformation.py ├── 4_11_combined_transformations.py ├── 4_12_combined_transformations_reversed.py ├── 4_13_calculating_determinant.py ├── 4_14_determinant_shear.py ├── 4_15_negative_determinant.py ├── 4_16_zero_determinant.py ├── 4_17_inverse_identity_sympy.py ├── 4_18_numpy_system_of_equations.py ├── 4_19_sympy_system_of_equations.py ├── 4_20_eigendecomposition. ├── 4_20_eigendecomposition.py └── 4_21_decompose_and_recompose.py ├── chapter_5 ├── 5_01_linear_regression_sklearn.py ├── 5_02_residuals.py ├── 5_04_sum_of_squares.py ├── 5_05_simple_linear_regression_formula.py ├── 5_06_inverse_transpose_technique.py ├── 5_07_qr_decomposition.py ├── 5_08_gradient_descent_parabola.py ├── 5_09_gradient_descent_linear_regression.py ├── 5_10_partial_derivatives_m_and_b.py ├── 5_11_linear_regression_sympy.py ├── 5_12_loss_function_plot.py ├── 5_13_stochastic_gradient_descent_linear_regression.py ├── 5_14_correlation_coefficient.py ├── 5_15_correlation_coefficient_from_scratch.py ├── 5_16_critical_value_t_distribution.py ├── 5_17_statistical_significance_linear_regression.py ├── 5_18_correlation_matrix.py ├── 5_19_standard_error_estimate.py ├── 5_20_prediction_interval.py ├── 5_21_train_test_split.py ├── 5_22_three_fold_cross_validation.py ├── 5_23_random_fold_validation.py ├── 5_24_multiple_linear_regression.py └── 5_3_residuals_each_point.txt ├── chapter_6 ├── .DS_Store ├── 6_01_logistic_function.py ├── 6_02_sympy_logistic_function.py ├── 6_03_logistic_function_sympy.py ├── 6_04_joint_likelihood.py ├── 6_05_joint_likelihood_2.py ├── 6_06_joint_likelihood_3.py ├── 6_07_joint_likelihood_sympy.py ├── 6_08_logistic_regression_gradient_descent.py ├── 6_09_logistic_regression_multivariable.py ├── 6_10_log_likelihood_fit.py ├── 6_11_log_likelihood_fit_2.py ├── 6_12_log_likelihood_patients.py ├── 6_13_log_likelihood_patients_2.py ├── 6_14_rsquare_logistic_regression.py ├── 6_15_pvalue_logistic_regression.py ├── 6_16_logistic_regression_3_fold.py ├── 6_17_confusion_matrix.py └── 6_18_roc_auc.py ├── chapter_7 ├── .DS_Store ├── 7_01_relu_activation_function.py ├── 7_02_logistic_activation_function.py ├── 7_03_forward_propagation.py ├── 7_07_cost_derivative.py ├── 7_08_derivative_a2_z2.py ├── 7_09_derivative_z2_w2.py ├── 7_10_all_derivatives.py ├── 7_11_full_stochastic_gradient_descent_neural_network.py ├── 7_12_with_interactive_shell.py └── 7_13_sklearn_neural_network_classifier.py └── chapter_8 ├── 8_02_sql_in_python.py ├── 8_03_pandas_and_sql.py └── 8_1_sql_example.sql /README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/thomasnield/oreilly_essential_math_for_data_science_book/HEAD/README.md -------------------------------------------------------------------------------- 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