| Appendix._Central_tendency.mp4 | 10.2 MB | ||
| Appendix._Distributions.mp4 | 9.7 MB | ||
| Appendix._Logarithms.mp4 | 9.2 MB | ||
| Appendix._Measures_of_dispersion.mp4 | 21.2 MB | ||
| Appendix._Measures_of_the_relationships_between_variables.mp4 | 10.7 MB | ||
| Appendix._Refresher_on_statistical_concepts.mp4 | 17.4 MB | ||
| Appendix._Sigma_notation.mp4 | 5.3 MB | ||
| Appendix.__Vectors.mp4 | 5.8 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Chapter_1._Classes_of_machine_learning_algorithms.mp4 | 40.5 MB | ||
| Chapter_1._Introduction_to_machine_learning.mp4 | 34.1 MB | ||
| Chapter_1._Summary.mp4 | 5.4 MB | ||
| Chapter_1._Thinking_about_the_ethical_impact_of_machine_learning.mp4 | 20.4 MB | ||
| Chapter_1._What_will_you_learn_in_this_book.mp4 | 2.6 MB | ||
| Chapter_1._Which_datasets_will_we_use.mp4 | 2 MB | ||
| Chapter_1._Why_use_R_for_machine_learning.mp4 | 8 MB | ||
| Chapter_10._Building_your_first_GAM.mp4 | 19.1 MB | ||
| Chapter_10._More_flexibility_Splines_and_generalized_additive_models.mp4 | 20.7 MB | ||
| Chapter_10._Strengths_and_weaknesses_of_GAMs.mp4 | 3.8 MB | ||
| Chapter_10._Summary.mp4 | 2.6 MB | ||
| Chapter_10.__Nonlinear_regression_with_generalized_additive_models.mp4 | 18.5 MB | ||
| Chapter_11._Benchmarking_ridge,_LASSO,_elastic_net,_and_OLS_against_each_other.mp4 | 7.3 MB | ||
| Chapter_11._Building_your_first_ridge,_LASSO,_and_elastic_net_models.mp4 | 51.4 MB | ||
| Chapter_11._Preventing_overfitting_with_ridge_regression,_LASSO,_and_elastic_net.mp4 | 7 MB | ||
| Chapter_11._Strengths_and_weaknesses_of_ridge,_LASSO,_and_elastic_net.mp4 | 4.9 MB | ||
| Chapter_11._Summary.mp4 | 4.8 MB | ||
| Chapter_11._What_is_elastic_net.mp4 | 11.2 MB | ||
| Chapter_11._What_is_ridge_regression.mp4 | 18.5 MB | ||
| Chapter_11._What_is_the_L1_norm,_and_how_does_LASSO_use_it.mp4 | 8.3 MB | ||
| Chapter_11._What_is_the_L2_norm,_and_how_does_ridge_regression_use_it.mp4 | 18.6 MB | ||
| Chapter_12._Benchmarking_the_kNN,_random_forest,_and_XGBoost_model-building_processes.mp4 | 4.4 MB | ||
| Chapter_12._Building_your_first_XGBoost_regression_model.mp4 | 12.2 MB | ||
| Chapter_12._Building_your_first_kNN_regression_model.mp4 | 32.3 MB | ||
| Chapter_12._Building_your_first_random_forest_regression_model.mp4 | 9.8 MB | ||
| Chapter_12._Regression_with_kNN,_random_forest,_and_XGBoost.mp4 | 14.1 MB | ||
| Chapter_12._Strengths_and_weaknesses_of_kNN,_random_forest,_and_XGBoost.mp4 | 2.5 MB | ||
| Chapter_12._Summary.mp4 | 3.7 MB | ||
| Chapter_12._Using_tree-based_learners_to_predict_a_continuous_variable.mp4 | 12.3 MB | ||
| Chapter_13._Building_your_first_PCA_model.mp4 | 43.6 MB | ||
| Chapter_13._Maximizing_variance_with_principal_component_analysis.mp4 | 31.4 MB | ||
| Chapter_13._Strengths_and_weaknesses_of_PCA.mp4 | 2.7 MB | ||
| Chapter_13._Summary.mp4 | 3.7 MB | ||
| Chapter_13._What_is_principal_component_analysis.mp4 | 27.5 MB | ||
| Chapter_14._Building_your_first_UMAP_model.mp4 | 17.4 MB | ||
| Chapter_14._Building_your_first_t-SNE_embedding.mp4 | 25.2 MB | ||
| Chapter_14._Maximizing_similarity_with_t-SNE_and_UMAP.mp4 | 35.2 MB | ||
| Chapter_14._Strengths_and_weaknesses_of_t-SNE_and_UMAP.mp4 | 3.4 MB | ||
| Chapter_14._Summary.mp4 | 3.2 MB | ||
| Chapter_14._What_is_UMAP.mp4 | 16.5 MB | ||
| Chapter_15._Building_an_LLE_of_our_flea_data.mp4 | 5.5 MB | ||
| Chapter_15._Building_your_first_LLE.mp4 | 19 MB | ||
| Chapter_15._Building_your_first_SOM.mp4 | 61.8 MB | ||
| Chapter_15._Self-organizing_maps_and_locally_linear_embedding.mp4 | 12.6 MB | ||
| Chapter_15._Strengths_and_weaknesses_of_SOMs_and_LLE.mp4 | 5.6 MB | ||
| Chapter_15._Summary.mp4 | 3.9 MB | ||
| Chapter_15._What_are_self-organizing_maps.mp4 | 31.1 MB | ||
| Chapter_15._What_is_locally_linear_embedding.mp4 | 11.4 MB | ||
| Chapter_16._Building_your_first_k-means_model.mp4 | 81.9 MB | ||
| Chapter_16._Clustering_by_finding_centers_with_k-means.mp4 | 32.8 MB | ||
| Chapter_16._Strengths_and_weaknesses_of_k-means_clustering.mp4 | 3.4 MB | ||
| Chapter_16._Summary.mp4 | 2.8 MB | ||
| Chapter_17._Building_your_first_agglomerative_hierarchical_clustering_model.mp4 | 56.6 MB | ||
| Chapter_17._Hierarchical_clustering.mp4 | 33.9 MB | ||
| Chapter_17._How_stable_are_our_clusters.mp4 | 11.5 MB | ||
| Chapter_17._Strengths_and_weaknesses_of_hierarchical_clustering.mp4 | 6 MB | ||
| Chapter_17._Summary.mp4 | 3.8 MB | ||
| Chapter_18._Building_your_first_DBSCAN_model.mp4 | 69.8 MB | ||
| Chapter_18._Building_your_first_OPTICS_model.mp4 | 9.8 MB | ||
| Chapter_18._Clustering_based_on_density_DBSCAN_and_OPTICS.mp4 | 54.7 MB | ||
| Chapter_18._Strengths_and_weaknesses_of_density-based_clustering.mp4 | 3.6 MB | ||
| Chapter_18._Summary.mp4 | 5 MB | ||
| Chapter_19._Building_your_first_Gaussian_mixture_model_for_clustering.mp4 | 20.3 MB | ||
| Chapter_19._Clustering_based_on_distributions_with_mixture_modeling.mp4 | 44.5 MB | ||
| Chapter_19._Strengths_and_weaknesses_of_mixture_model_clustering.mp4 | 4.5 MB | ||
| Chapter_19._Summary.mp4 | 3.7 MB | ||
| Chapter_2._Loading_the_tidyverse.mp4 | 536.9 KB | ||
| Chapter_2._Summary.mp4 | 7.5 MB | ||
| Chapter_2._Tidying,_manipulating,_and_plotting_data_with_the_tidyverse.mp4 | 14.4 MB | ||
| Chapter_2._What_the_dplyr_package_is_and_what_it_does.mp4 | 19 MB | ||
| Chapter_2._What_the_ggplot2_package_is_and_what_it_does.mp4 | 15.8 MB | ||
| Chapter_2._What_the_purrr_package_is_and_what_it_does.mp4 | 25.3 MB | ||
| Chapter_2._What_the_tibble_package_is_and_what_it_does.mp4 | 12.2 MB | ||
| Chapter_2._What_the_tidyr_package_is_and_what_it_does.mp4 | 7.4 MB | ||
| Chapter_20._Final_notes_and_further_reading.mp4 | 65.8 MB | ||
| Chapter_20._The_last_word.mp4 | 1.4 MB | ||
| Chapter_20._Where_can_you_go_from_here.mp4 | 22.1 MB | ||
| Chapter_3._Balancing_two_sources_of_model_error_The_bias-variance_trade-off.mp4 | 16 MB | ||
| Chapter_3._Building_your_first_kNN_model.mp4 | 26 MB | ||
| Chapter_3._Classifying_based_on_similarities_with_k-nearest_neighbors.mp4 | 22.8 MB | ||
| Chapter_3._Cross-validating_our_kNN_model.mp4 | 39.5 MB | ||
| Chapter_3._Strengths_and_weaknesses_of_kNN.mp4 | 5.5 MB | ||
| Chapter_3._Summary.mp4 | 9.3 MB | ||
| Chapter_3._Tuning_k_to_improve_the_model.mp4 | 23 MB | ||
| Chapter_3._Using_cross-validation_to_tell_if_we_re_overfitting_or_underfitting.mp4 | 6.6 MB | ||
| Chapter_3._What_algorithms_can_learn,_and_what_they_must_be_told_Parameters-_s_and_hyperparameters.mp4 | 10.7 MB | ||
| Chapter_4._Building_your_first_logistic_regression_model.mp4 | 40.8 MB | ||
| Chapter_4._Classifying_based_on_odds_with_logistic_regression.mp4 | 55.3 MB | ||
| Chapter_4._Cross-validating_the_logistic_regression_model.mp4 | 11.4 MB | ||
| Chapter_4._Interpreting_the_model_The_odds_ratio.mp4 | 11.6 MB | ||
| Chapter_4._Strengths_and_weaknesses_of_logistic_regression.mp4 | 5 MB | ||
| Chapter_4._Summary.mp4 | 6.8 MB | ||
| Chapter_4._Using_our_model_to_make_predictions.mp4 | 2.3 MB | ||
| Chapter_5._Building_your_first_linear_and_quadratic_discriminant_models.mp4 | 21 MB | ||
| Chapter_5._Classifying_by_maximizing_separation_with_discriminant_analysis.mp4 | 56.8 MB | ||
| Chapter_5._Strengths_and_weaknesses_of_LDA_and_QDA.mp4 | 4.9 MB | ||
| Chapter_5._Summary.mp4 | 5.5 MB | ||
| Chapter_6._Building_your_first_SVM_model.mp4 | 33 MB | ||
| Chapter_6._Building_your_first_naive_Bayes_model.mp4 | 17.1 MB | ||
| Chapter_6._Classifying_with_naive_Bayes_and_support_vector_machines.mp4 | 31.9 MB | ||
| Chapter_6._Cross-validating_our_SVM_model.mp4 | 7 MB | ||
| Chapter_6._Strengths_and_weaknesses_of_naive_Bayes.mp4 | 2.8 MB | ||
| Chapter_6._Strengths_and_weaknesses_of_the_SVM_algorithm.mp4 | 3.5 MB | ||
| Chapter_6._Summary.mp4 | 5.9 MB | ||
| Chapter_6._What_is_the_support_vector_machine_(SVM)_algorithm.mp4 | 59.4 MB | ||
| Chapter_7._Building_your_first_decision_tree_model.mp4 | 2.8 MB | ||
| Chapter_7._Classifying_with_decision_trees.mp4 | 50.2 MB | ||
| Chapter_7._Cross-validating_our_decision_tree_model.mp4 | 7.3 MB | ||
| Chapter_7._Loading_and_exploring_the_zoo_dataset.mp4 | 3.1 MB | ||
| Chapter_7._Strengths_and_weaknesses_of_tree-based_algorithms.mp4 | 1.8 MB | ||
| Chapter_7._Summary.mp4 | 2.2 MB | ||
| Chapter_7._Training_the_decision_tree_model.mp4 | 30 MB | ||
| Chapter_8._Benchmarking_algorithms_against_each_other.mp4 | 7 MB | ||
| Chapter_8._Building_your_first_XGBoost_model.mp4 | 21.6 MB | ||
| Chapter_8._Building_your_first_random_forest_model.mp4 | 12.8 MB | ||
| Chapter_8._Improving_decision_trees_with_random_forests_and_boosting.mp4 | 59.7 MB | ||
| Chapter_8._Strengths_and_weaknesses_of_tree-based_algorithms.mp4 | 3 MB | ||
| Chapter_8._Summary.mp4 | 3.4 MB | ||
| Chapter_9._Building_your_first_linear_regression_model.mp4 | 120.1 MB | ||
| Chapter_9._Linear_regression.mp4 | 49.1 MB | ||
| Chapter_9._Strengths_and_weaknesses_of_linear_regression.mp4 | 3.1 MB | ||
| Chapter_9._Summary.mp4 | 3.9 MB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Part_1._Introduction.mp4 | 5.4 MB | ||
| Part_2._Classification.mp4 | 5.3 MB | ||
| Part_3._Regression.mp4 | 4.3 MB | ||
| Part_4._Dimension_reduction.mp4 | 3.6 MB | ||
| Part_5._Clustering.mp4 | 3 MB | ||
| ▲ 137 total files | |||
Machine Learning with R, the tidyverse, and mlr. Video Edition
https://FreeCourseWeb.com
Released 4/2020
By Hefin Rhys
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 15h 49m | Size: 2.37 GB
Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts
Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of academic data scientists, machine learning has become a mainstream business process, and tools like the easy-to-learn R programming language put high-quality data analysis in the hands of any programmer. Machine Learning with R, the tidyverse, and mlr teaches you widely used ML techniques and how to apply them to your own datasets using the R programming language and its powerful ecosystem of tools. This book will get you started!
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