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Files:
  1. Get Bonus Downloads Here.url 183 bytes
  2. Appendix._Central_tendency.mp4 10.2 MB
  3. Appendix._Distributions.mp4 9.7 MB
  4. Appendix._Logarithms.mp4 9.2 MB
  5. Appendix._Measures_of_dispersion.mp4 21.2 MB
  6. Appendix._Measures_of_the_relationships_between_variables.mp4 10.7 MB
  7. Appendix._Refresher_on_statistical_concepts.mp4 17.4 MB
  8. Appendix._Sigma_notation.mp4 5.3 MB
  9. Appendix.__Vectors.mp4 5.8 MB
  10. Bonus Resources.txt 386 bytes
  11. Chapter_1._Classes_of_machine_learning_algorithms.mp4 40.5 MB
  12. Chapter_1._Introduction_to_machine_learning.mp4 34.1 MB
  13. Chapter_1._Summary.mp4 5.4 MB
  14. Chapter_1._Thinking_about_the_ethical_impact_of_machine_learning.mp4 20.4 MB
  15. Chapter_1._What_will_you_learn_in_this_book.mp4 2.6 MB
  16. Chapter_1._Which_datasets_will_we_use.mp4 2.0 MB
  17. Chapter_1._Why_use_R_for_machine_learning.mp4 8.0 MB
  18. Chapter_10._Building_your_first_GAM.mp4 19.1 MB
  19. Chapter_10._More_flexibility_Splines_and_generalized_additive_models.mp4 20.7 MB
  20. Chapter_10._Strengths_and_weaknesses_of_GAMs.mp4 3.8 MB
  21. Chapter_10._Summary.mp4 2.6 MB
  22. Chapter_10.__Nonlinear_regression_with_generalized_additive_models.mp4 18.5 MB
  23. Chapter_11._Benchmarking_ridge,_LASSO,_elastic_net,_and_OLS_against_each_other.mp4 7.3 MB
  24. Chapter_11._Building_your_first_ridge,_LASSO,_and_elastic_net_models.mp4 51.4 MB
  25. Chapter_11._Preventing_overfitting_with_ridge_regression,_LASSO,_and_elastic_net.mp4 7.0 MB
  26. Chapter_11._Strengths_and_weaknesses_of_ridge,_LASSO,_and_elastic_net.mp4 4.9 MB
  27. Chapter_11._Summary.mp4 4.8 MB
  28. Chapter_11._What_is_elastic_net.mp4 11.2 MB
  29. Chapter_11._What_is_ridge_regression.mp4 18.5 MB
  30. Chapter_11._What_is_the_L1_norm,_and_how_does_LASSO_use_it.mp4 8.3 MB
  31. Chapter_11._What_is_the_L2_norm,_and_how_does_ridge_regression_use_it.mp4 18.6 MB
  32. Chapter_12._Benchmarking_the_kNN,_random_forest,_and_XGBoost_model-building_processes.mp4 4.4 MB
  33. Chapter_12._Building_your_first_XGBoost_regression_model.mp4 12.2 MB
  34. Chapter_12._Building_your_first_kNN_regression_model.mp4 32.3 MB
  35. Chapter_12._Building_your_first_random_forest_regression_model.mp4 9.8 MB
  36. Chapter_12._Regression_with_kNN,_random_forest,_and_XGBoost.mp4 14.1 MB
  37. Chapter_12._Strengths_and_weaknesses_of_kNN,_random_forest,_and_XGBoost.mp4 2.5 MB
  38. Chapter_12._Summary.mp4 3.7 MB
  39. Chapter_12._Using_tree-based_learners_to_predict_a_continuous_variable.mp4 12.3 MB
  40. Chapter_13._Building_your_first_PCA_model.mp4 43.6 MB
  41. Chapter_13._Maximizing_variance_with_principal_component_analysis.mp4 31.4 MB
  42. Chapter_13._Strengths_and_weaknesses_of_PCA.mp4 2.7 MB
  43. Chapter_13._Summary.mp4 3.7 MB
  44. Chapter_13._What_is_principal_component_analysis.mp4 27.5 MB
  45. Chapter_14._Building_your_first_UMAP_model.mp4 17.4 MB
  46. Chapter_14._Building_your_first_t-SNE_embedding.mp4 25.2 MB
  47. Chapter_14._Maximizing_similarity_with_t-SNE_and_UMAP.mp4 35.2 MB
  48. Chapter_14._Strengths_and_weaknesses_of_t-SNE_and_UMAP.mp4 3.4 MB
  49. Chapter_14._Summary.mp4 3.2 MB
  50. Chapter_14._What_is_UMAP.mp4 16.5 MB
  51. Chapter_15._Building_an_LLE_of_our_flea_data.mp4 5.5 MB
  52. Chapter_15._Building_your_first_LLE.mp4 19.0 MB
  53. Chapter_15._Building_your_first_SOM.mp4 61.8 MB
  54. Chapter_15._Self-organizing_maps_and_locally_linear_embedding.mp4 12.6 MB
  55. Chapter_15._Strengths_and_weaknesses_of_SOMs_and_LLE.mp4 5.6 MB
  56. Chapter_15._Summary.mp4 3.9 MB
  57. Chapter_15._What_are_self-organizing_maps.mp4 31.1 MB
  58. Chapter_15._What_is_locally_linear_embedding.mp4 11.4 MB
  59. Chapter_16._Building_your_first_k-means_model.mp4 81.9 MB
  60. Chapter_16._Clustering_by_finding_centers_with_k-means.mp4 32.8 MB
  61. Chapter_16._Strengths_and_weaknesses_of_k-means_clustering.mp4 3.4 MB
  62. Chapter_16._Summary.mp4 2.8 MB
  63. Chapter_17._Building_your_first_agglomerative_hierarchical_clustering_model.mp4 56.6 MB
  64. Chapter_17._Hierarchical_clustering.mp4 33.9 MB
  65. Chapter_17._How_stable_are_our_clusters.mp4 11.5 MB
  66. Chapter_17._Strengths_and_weaknesses_of_hierarchical_clustering.mp4 6.0 MB
  67. Chapter_17._Summary.mp4 3.8 MB
  68. Chapter_18._Building_your_first_DBSCAN_model.mp4 69.8 MB
  69. Chapter_18._Building_your_first_OPTICS_model.mp4 9.8 MB
  70. Chapter_18._Clustering_based_on_density_DBSCAN_and_OPTICS.mp4 54.7 MB
  71. Chapter_18._Strengths_and_weaknesses_of_density-based_clustering.mp4 3.6 MB
  72. Chapter_18._Summary.mp4 5.0 MB
  73. Chapter_19._Building_your_first_Gaussian_mixture_model_for_clustering.mp4 20.3 MB
  74. Chapter_19._Clustering_based_on_distributions_with_mixture_modeling.mp4 44.5 MB
  75. Chapter_19._Strengths_and_weaknesses_of_mixture_model_clustering.mp4 4.5 MB
  76. Chapter_19._Summary.mp4 3.7 MB
  77. Chapter_2._Loading_the_tidyverse.mp4 536.9 KB
  78. Chapter_2._Summary.mp4 7.5 MB
  79. Chapter_2._Tidying,_manipulating,_and_plotting_data_with_the_tidyverse.mp4 14.4 MB
  80. Chapter_2._What_the_dplyr_package_is_and_what_it_does.mp4 19.0 MB
  81. Chapter_2._What_the_ggplot2_package_is_and_what_it_does.mp4 15.8 MB
  82. Chapter_2._What_the_purrr_package_is_and_what_it_does.mp4 25.3 MB
  83. Chapter_2._What_the_tibble_package_is_and_what_it_does.mp4 12.2 MB
  84. Chapter_2._What_the_tidyr_package_is_and_what_it_does.mp4 7.4 MB
  85. Chapter_20._Final_notes_and_further_reading.mp4 65.8 MB
  86. Chapter_20._The_last_word.mp4 1.4 MB
  87. Chapter_20._Where_can_you_go_from_here.mp4 22.1 MB
  88. Chapter_3._Balancing_two_sources_of_model_error_The_bias-variance_trade-off.mp4 16.0 MB
  89. Chapter_3._Building_your_first_kNN_model.mp4 26.0 MB
  90. Chapter_3._Classifying_based_on_similarities_with_k-nearest_neighbors.mp4 22.8 MB
  91. Chapter_3._Cross-validating_our_kNN_model.mp4 39.5 MB
  92. Chapter_3._Strengths_and_weaknesses_of_kNN.mp4 5.5 MB
  93. Chapter_3._Summary.mp4 9.3 MB
  94. Chapter_3._Tuning_k_to_improve_the_model.mp4 23.0 MB
  95. Chapter_3._Using_cross-validation_to_tell_if_we_re_overfitting_or_underfitting.mp4 6.6 MB
  96. Chapter_3._What_algorithms_can_learn,_and_what_they_must_be_told_Parameters-_s_and_hyperparameters.mp4 10.7 MB
  97. Chapter_4._Building_your_first_logistic_regression_model.mp4 40.8 MB
  98. Chapter_4._Classifying_based_on_odds_with_logistic_regression.mp4 55.3 MB
  99. Chapter_4._Cross-validating_the_logistic_regression_model.mp4 11.4 MB
  100. Chapter_4._Interpreting_the_model_The_odds_ratio.mp4 11.6 MB
  101. Chapter_4._Strengths_and_weaknesses_of_logistic_regression.mp4 5.0 MB
  102. Chapter_4._Summary.mp4 6.8 MB
  103. Chapter_4._Using_our_model_to_make_predictions.mp4 2.3 MB
  104. Chapter_5._Building_your_first_linear_and_quadratic_discriminant_models.mp4 21.0 MB
  105. Chapter_5._Classifying_by_maximizing_separation_with_discriminant_analysis.mp4 56.8 MB
  106. Chapter_5._Strengths_and_weaknesses_of_LDA_and_QDA.mp4 4.9 MB
  107. Chapter_5._Summary.mp4 5.5 MB
  108. Chapter_6._Building_your_first_SVM_model.mp4 33.0 MB
  109. Chapter_6._Building_your_first_naive_Bayes_model.mp4 17.1 MB
  110. Chapter_6._Classifying_with_naive_Bayes_and_support_vector_machines.mp4 31.9 MB
  111. Chapter_6._Cross-validating_our_SVM_model.mp4 7.0 MB
  112. Chapter_6._Strengths_and_weaknesses_of_naive_Bayes.mp4 2.8 MB
  113. Chapter_6._Strengths_and_weaknesses_of_the_SVM_algorithm.mp4 3.5 MB
  114. Chapter_6._Summary.mp4 5.9 MB
  115. Chapter_6._What_is_the_support_vector_machine_(SVM)_algorithm.mp4 59.4 MB
  116. Chapter_7._Building_your_first_decision_tree_model.mp4 2.8 MB
  117. Chapter_7._Classifying_with_decision_trees.mp4 50.2 MB
  118. Chapter_7._Cross-validating_our_decision_tree_model.mp4 7.3 MB
  119. Chapter_7._Loading_and_exploring_the_zoo_dataset.mp4 3.1 MB
  120. Chapter_7._Strengths_and_weaknesses_of_tree-based_algorithms.mp4 1.8 MB
  121. Chapter_7._Summary.mp4 2.2 MB
  122. Chapter_7._Training_the_decision_tree_model.mp4 30.0 MB
  123. Chapter_8._Benchmarking_algorithms_against_each_other.mp4 7.0 MB
  124. Chapter_8._Building_your_first_XGBoost_model.mp4 21.6 MB
  125. Chapter_8._Building_your_first_random_forest_model.mp4 12.8 MB
  126. Chapter_8._Improving_decision_trees_with_random_forests_and_boosting.mp4 59.7 MB
  127. Chapter_8._Strengths_and_weaknesses_of_tree-based_algorithms.mp4 3.0 MB
  128. Chapter_8._Summary.mp4 3.4 MB
  129. Chapter_9._Building_your_first_linear_regression_model.mp4 120.1 MB
  130. Chapter_9._Linear_regression.mp4 49.1 MB
  131. Chapter_9._Strengths_and_weaknesses_of_linear_regression.mp4 3.1 MB
  132. Chapter_9._Summary.mp4 3.9 MB
  133. Part_1._Introduction.mp4 5.4 MB
  134. Part_2._Classification.mp4 5.3 MB
  135. Part_3._Regression.mp4 4.3 MB
  136. Part_4._Dimension_reduction.mp4 3.6 MB
  137. Part_5._Clustering.mp4 3.0 MB

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