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Appendix._Central_tendency.mp4
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MP4
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10.2 MB
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Appendix._Distributions.mp4
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MP4
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9.7 MB
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Appendix._Logarithms.mp4
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MP4
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9.2 MB
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Appendix._Measures_of_dispersion.mp4
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MP4
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21.2 MB
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Appendix._Measures_of_the_relationships_between_variables.mp4
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MP4
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10.7 MB
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Appendix._Refresher_on_statistical_concepts.mp4
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MP4
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17.4 MB
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Appendix._Sigma_notation.mp4
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MP4
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5.3 MB
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Appendix.__Vectors.mp4
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MP4
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5.8 MB
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Bonus Resources.txt
|
TXT
|
409.6 B
|
|
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Chapter_1._Classes_of_machine_learning_algorithms.mp4
|
MP4
|
40.5 MB
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Chapter_1._Introduction_to_machine_learning.mp4
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MP4
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34.1 MB
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Chapter_1._Summary.mp4
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MP4
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5.4 MB
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Chapter_1._Thinking_about_the_ethical_impact_of_machine_learning.mp4
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MP4
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20.4 MB
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Chapter_1._What_will_you_learn_in_this_book.mp4
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MP4
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2.6 MB
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Chapter_1._Which_datasets_will_we_use.mp4
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MP4
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2 MB
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Chapter_1._Why_use_R_for_machine_learning.mp4
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MP4
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8 MB
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Chapter_10._Building_your_first_GAM.mp4
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MP4
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19.1 MB
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Chapter_10._More_flexibility_Splines_and_generalized_additive_models.mp4
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MP4
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20.7 MB
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Chapter_10._Strengths_and_weaknesses_of_GAMs.mp4
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MP4
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3.8 MB
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Chapter_10._Summary.mp4
|
MP4
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2.6 MB
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Chapter_10.__Nonlinear_regression_with_generalized_additive_models.mp4
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MP4
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18.5 MB
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Chapter_11._Benchmarking_ridge,_LASSO,_elastic_net,_and_OLS_against_each_other.mp4
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MP4
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7.3 MB
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Chapter_11._Building_your_first_ridge,_LASSO,_and_elastic_net_models.mp4
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MP4
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51.4 MB
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Chapter_11._Preventing_overfitting_with_ridge_regression,_LASSO,_and_elastic_net.mp4
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MP4
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7 MB
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Chapter_11._Strengths_and_weaknesses_of_ridge,_LASSO,_and_elastic_net.mp4
|
MP4
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4.9 MB
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Chapter_11._Summary.mp4
|
MP4
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4.8 MB
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Chapter_11._What_is_elastic_net.mp4
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MP4
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11.2 MB
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Chapter_11._What_is_ridge_regression.mp4
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MP4
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18.5 MB
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Chapter_11._What_is_the_L1_norm,_and_how_does_LASSO_use_it.mp4
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MP4
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8.3 MB
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Chapter_11._What_is_the_L2_norm,_and_how_does_ridge_regression_use_it.mp4
|
MP4
|
18.6 MB
|
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Chapter_12._Benchmarking_the_kNN,_random_forest,_and_XGBoost_model-building_processes.mp4
|
MP4
|
4.4 MB
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Chapter_12._Building_your_first_XGBoost_regression_model.mp4
|
MP4
|
12.2 MB
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Chapter_12._Building_your_first_kNN_regression_model.mp4
|
MP4
|
32.3 MB
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Chapter_12._Building_your_first_random_forest_regression_model.mp4
|
MP4
|
9.8 MB
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Chapter_12._Regression_with_kNN,_random_forest,_and_XGBoost.mp4
|
MP4
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14.1 MB
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Chapter_12._Strengths_and_weaknesses_of_kNN,_random_forest,_and_XGBoost.mp4
|
MP4
|
2.5 MB
|
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Chapter_12._Summary.mp4
|
MP4
|
3.7 MB
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Chapter_12._Using_tree-based_learners_to_predict_a_continuous_variable.mp4
|
MP4
|
12.3 MB
|
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Chapter_13._Building_your_first_PCA_model.mp4
|
MP4
|
43.6 MB
|
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Chapter_13._Maximizing_variance_with_principal_component_analysis.mp4
|
MP4
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31.4 MB
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Chapter_13._Strengths_and_weaknesses_of_PCA.mp4
|
MP4
|
2.7 MB
|
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Chapter_13._Summary.mp4
|
MP4
|
3.7 MB
|
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Chapter_13._What_is_principal_component_analysis.mp4
|
MP4
|
27.5 MB
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Chapter_14._Building_your_first_UMAP_model.mp4
|
MP4
|
17.4 MB
|
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Chapter_14._Building_your_first_t-SNE_embedding.mp4
|
MP4
|
25.2 MB
|
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Chapter_14._Maximizing_similarity_with_t-SNE_and_UMAP.mp4
|
MP4
|
35.2 MB
|
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Chapter_14._Strengths_and_weaknesses_of_t-SNE_and_UMAP.mp4
|
MP4
|
3.4 MB
|
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Chapter_14._Summary.mp4
|
MP4
|
3.2 MB
|
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Chapter_14._What_is_UMAP.mp4
|
MP4
|
16.5 MB
|
|
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Chapter_15._Building_an_LLE_of_our_flea_data.mp4
|
MP4
|
5.5 MB
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Chapter_15._Building_your_first_LLE.mp4
|
MP4
|
19 MB
|
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Chapter_15._Building_your_first_SOM.mp4
|
MP4
|
61.8 MB
|
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Chapter_15._Self-organizing_maps_and_locally_linear_embedding.mp4
|
MP4
|
12.6 MB
|
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Chapter_15._Strengths_and_weaknesses_of_SOMs_and_LLE.mp4
|
MP4
|
5.6 MB
|
|
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Chapter_15._Summary.mp4
|
MP4
|
3.9 MB
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Chapter_15._What_are_self-organizing_maps.mp4
|
MP4
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31.1 MB
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Chapter_15._What_is_locally_linear_embedding.mp4
|
MP4
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11.4 MB
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Chapter_16._Building_your_first_k-means_model.mp4
|
MP4
|
81.9 MB
|
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Chapter_16._Clustering_by_finding_centers_with_k-means.mp4
|
MP4
|
32.8 MB
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Chapter_16._Strengths_and_weaknesses_of_k-means_clustering.mp4
|
MP4
|
3.4 MB
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Chapter_16._Summary.mp4
|
MP4
|
2.8 MB
|
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Chapter_17._Building_your_first_agglomerative_hierarchical_clustering_model.mp4
|
MP4
|
56.6 MB
|
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Chapter_17._Hierarchical_clustering.mp4
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MP4
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33.9 MB
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Chapter_17._How_stable_are_our_clusters.mp4
|
MP4
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11.5 MB
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Chapter_17._Strengths_and_weaknesses_of_hierarchical_clustering.mp4
|
MP4
|
6 MB
|
|
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Chapter_17._Summary.mp4
|
MP4
|
3.8 MB
|
|
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Chapter_18._Building_your_first_DBSCAN_model.mp4
|
MP4
|
69.8 MB
|
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Chapter_18._Building_your_first_OPTICS_model.mp4
|
MP4
|
9.8 MB
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Chapter_18._Clustering_based_on_density_DBSCAN_and_OPTICS.mp4
|
MP4
|
54.7 MB
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Chapter_18._Strengths_and_weaknesses_of_density-based_clustering.mp4
|
MP4
|
3.6 MB
|
|
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Chapter_18._Summary.mp4
|
MP4
|
5 MB
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Chapter_19._Building_your_first_Gaussian_mixture_model_for_clustering.mp4
|
MP4
|
20.3 MB
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Chapter_19._Clustering_based_on_distributions_with_mixture_modeling.mp4
|
MP4
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44.5 MB
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Chapter_19._Strengths_and_weaknesses_of_mixture_model_clustering.mp4
|
MP4
|
4.5 MB
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Chapter_19._Summary.mp4
|
MP4
|
3.7 MB
|
|
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Chapter_2._Loading_the_tidyverse.mp4
|
MP4
|
536.9 KB
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Chapter_2._Summary.mp4
|
MP4
|
7.5 MB
|
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Chapter_2._Tidying,_manipulating,_and_plotting_data_with_the_tidyverse.mp4
|
MP4
|
14.4 MB
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Chapter_2._What_the_dplyr_package_is_and_what_it_does.mp4
|
MP4
|
19 MB
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Chapter_2._What_the_ggplot2_package_is_and_what_it_does.mp4
|
MP4
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15.8 MB
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Chapter_2._What_the_purrr_package_is_and_what_it_does.mp4
|
MP4
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25.3 MB
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Chapter_2._What_the_tibble_package_is_and_what_it_does.mp4
|
MP4
|
12.2 MB
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Chapter_2._What_the_tidyr_package_is_and_what_it_does.mp4
|
MP4
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7.4 MB
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Chapter_20._Final_notes_and_further_reading.mp4
|
MP4
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65.8 MB
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Chapter_20._The_last_word.mp4
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MP4
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1.4 MB
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Chapter_20._Where_can_you_go_from_here.mp4
|
MP4
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22.1 MB
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Chapter_3._Balancing_two_sources_of_model_error_The_bias-variance_trade-off.mp4
|
MP4
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16 MB
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Chapter_3._Building_your_first_kNN_model.mp4
|
MP4
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26 MB
|
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Chapter_3._Classifying_based_on_similarities_with_k-nearest_neighbors.mp4
|
MP4
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22.8 MB
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Chapter_3._Cross-validating_our_kNN_model.mp4
|
MP4
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39.5 MB
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Chapter_3._Strengths_and_weaknesses_of_kNN.mp4
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MP4
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5.5 MB
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Chapter_3._Summary.mp4
|
MP4
|
9.3 MB
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Chapter_3._Tuning_k_to_improve_the_model.mp4
|
MP4
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23 MB
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Chapter_3._Using_cross-validation_to_tell_if_we_re_overfitting_or_underfitting.mp4
|
MP4
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6.6 MB
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Chapter_3._What_algorithms_can_learn,_and_what_they_must_be_told_Parameters-_s_and_hyperparameters.mp4
|
MP4
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10.7 MB
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Chapter_4._Building_your_first_logistic_regression_model.mp4
|
MP4
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40.8 MB
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Chapter_4._Classifying_based_on_odds_with_logistic_regression.mp4
|
MP4
|
55.3 MB
|
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Chapter_4._Cross-validating_the_logistic_regression_model.mp4
|
MP4
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11.4 MB
|
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Chapter_4._Interpreting_the_model_The_odds_ratio.mp4
|
MP4
|
11.6 MB
|
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Chapter_4._Strengths_and_weaknesses_of_logistic_regression.mp4
|
MP4
|
5 MB
|
|
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Chapter_4._Summary.mp4
|
MP4
|
6.8 MB
|
|
|
Chapter_4._Using_our_model_to_make_predictions.mp4
|
MP4
|
2.3 MB
|
|
|
Chapter_5._Building_your_first_linear_and_quadratic_discriminant_models.mp4
|
MP4
|
21 MB
|
|
|
Chapter_5._Classifying_by_maximizing_separation_with_discriminant_analysis.mp4
|
MP4
|
56.8 MB
|
|
|
Chapter_5._Strengths_and_weaknesses_of_LDA_and_QDA.mp4
|
MP4
|
4.9 MB
|
|
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Chapter_5._Summary.mp4
|
MP4
|
5.5 MB
|
|
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Chapter_6._Building_your_first_SVM_model.mp4
|
MP4
|
33 MB
|
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Chapter_6._Building_your_first_naive_Bayes_model.mp4
|
MP4
|
17.1 MB
|
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Chapter_6._Classifying_with_naive_Bayes_and_support_vector_machines.mp4
|
MP4
|
31.9 MB
|
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Chapter_6._Cross-validating_our_SVM_model.mp4
|
MP4
|
7 MB
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Chapter_6._Strengths_and_weaknesses_of_naive_Bayes.mp4
|
MP4
|
2.8 MB
|
|
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Chapter_6._Strengths_and_weaknesses_of_the_SVM_algorithm.mp4
|
MP4
|
3.5 MB
|
|
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Chapter_6._Summary.mp4
|
MP4
|
5.9 MB
|
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Chapter_6._What_is_the_support_vector_machine_(SVM)_algorithm.mp4
|
MP4
|
59.4 MB
|
|
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Chapter_7._Building_your_first_decision_tree_model.mp4
|
MP4
|
2.8 MB
|
|
|
Chapter_7._Classifying_with_decision_trees.mp4
|
MP4
|
50.2 MB
|
|
|
Chapter_7._Cross-validating_our_decision_tree_model.mp4
|
MP4
|
7.3 MB
|
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|
Chapter_7._Loading_and_exploring_the_zoo_dataset.mp4
|
MP4
|
3.1 MB
|
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Chapter_7._Strengths_and_weaknesses_of_tree-based_algorithms.mp4
|
MP4
|
1.8 MB
|
|
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Chapter_7._Summary.mp4
|
MP4
|
2.2 MB
|
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Chapter_7._Training_the_decision_tree_model.mp4
|
MP4
|
30 MB
|
|
|
Chapter_8._Benchmarking_algorithms_against_each_other.mp4
|
MP4
|
7 MB
|
|
|
Chapter_8._Building_your_first_XGBoost_model.mp4
|
MP4
|
21.6 MB
|
|
|
Chapter_8._Building_your_first_random_forest_model.mp4
|
MP4
|
12.8 MB
|
|
|
Chapter_8._Improving_decision_trees_with_random_forests_and_boosting.mp4
|
MP4
|
59.7 MB
|
|
|
Chapter_8._Strengths_and_weaknesses_of_tree-based_algorithms.mp4
|
MP4
|
3 MB
|
|
|
Chapter_8._Summary.mp4
|
MP4
|
3.4 MB
|
|
|
Chapter_9._Building_your_first_linear_regression_model.mp4
|
MP4
|
120.1 MB
|
|
|
Chapter_9._Linear_regression.mp4
|
MP4
|
49.1 MB
|
|
|
Chapter_9._Strengths_and_weaknesses_of_linear_regression.mp4
|
MP4
|
3.1 MB
|
|
|
Chapter_9._Summary.mp4
|
MP4
|
3.9 MB
|
|
|
Get Bonus Downloads Here.url
|
URL
|
204.8 B
|
|
|
Part_1._Introduction.mp4
|
MP4
|
5.4 MB
|
|
|
Part_2._Classification.mp4
|
MP4
|
5.3 MB
|
|
|
Part_3._Regression.mp4
|
MP4
|
4.3 MB
|
|
|
Part_4._Dimension_reduction.mp4
|
MP4
|
3.6 MB
|
|
|
Part_5._Clustering.mp4
|
MP4
|
3 MB
|
