FreeCourseWeb com Machine Learning with the tidyverse and mlr Video Edition

FreeCourseWeb com Machine Learning with the tidyverse and mlr Video Edition

Name	[ FreeCourseWeb.com ] Machine Learning with R, the tidyverse, and mlr. Video Edition	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	2.6 GB
Total Files	137
Last Seen	2025-04-30 23:47
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Appendix._Central_tendency.mp4	10.7 MB
Appendix._Distributions.mp4	10.2 MB
Appendix._Logarithms.mp4	9.7 MB
Appendix._Measures_of_dispersion.mp4	22.3 MB
Appendix._Measures_of_the_relationships_between_variables.mp4	11.2 MB
Appendix._Refresher_on_statistical_concepts.mp4	18.2 MB
Appendix._Sigma_notation.mp4	5.6 MB
Appendix.__Vectors.mp4	6.1 MB
Bonus Resources.txt	0.4 KB
Chapter_1._Classes_of_machine_learning_algorithms.mp4	42.4 MB
Chapter_1._Introduction_to_machine_learning.mp4	35.8 MB
Chapter_1._Summary.mp4	5.7 MB
Chapter_1._Thinking_about_the_ethical_impact_of_machine_learning.mp4	21.4 MB
Chapter_1._What_will_you_learn_in_this_book.mp4	2.8 MB
Chapter_1._Which_datasets_will_we_use.mp4	2.1 MB
Chapter_1._Why_use_R_for_machine_learning.mp4	8.3 MB
Chapter_10._Building_your_first_GAM.mp4	20.0 MB
Chapter_10._More_flexibility_Splines_and_generalized_additive_models.mp4	21.8 MB
Chapter_10._Strengths_and_weaknesses_of_GAMs.mp4	4.0 MB
Chapter_10._Summary.mp4	2.7 MB
Chapter_10.__Nonlinear_regression_with_generalized_additive_models.mp4	19.4 MB
Chapter_11._Benchmarking_ridge,_LASSO,_elastic_net,_and_OLS_against_each_other.mp4	7.6 MB
Chapter_11._Building_your_first_ridge,_LASSO,_and_elastic_net_models.mp4	53.9 MB
Chapter_11._Preventing_overfitting_with_ridge_regression,_LASSO,_and_elastic_net.mp4	7.3 MB
Chapter_11._Strengths_and_weaknesses_of_ridge,_LASSO,_and_elastic_net.mp4	5.2 MB
Chapter_11._Summary.mp4	5.0 MB
Chapter_11._What_is_elastic_net.mp4	11.8 MB
Chapter_11._What_is_ridge_regression.mp4	19.4 MB
Chapter_11._What_is_the_L1_norm,_and_how_does_LASSO_use_it.mp4	8.7 MB
Chapter_11._What_is_the_L2_norm,_and_how_does_ridge_regression_use_it.mp4	19.5 MB
Chapter_12._Benchmarking_the_kNN,_random_forest,_and_XGBoost_model-building_processes.mp4	4.6 MB
Chapter_12._Building_your_first_XGBoost_regression_model.mp4	12.8 MB
Chapter_12._Building_your_first_kNN_regression_model.mp4	33.9 MB
Chapter_12._Building_your_first_random_forest_regression_model.mp4	10.3 MB
Chapter_12._Regression_with_kNN,_random_forest,_and_XGBoost.mp4	14.8 MB
Chapter_12._Strengths_and_weaknesses_of_kNN,_random_forest,_and_XGBoost.mp4	2.7 MB
Chapter_12._Summary.mp4	3.9 MB
Chapter_12._Using_tree-based_learners_to_predict_a_continuous_variable.mp4	12.9 MB
Chapter_13._Building_your_first_PCA_model.mp4	45.7 MB
Chapter_13._Maximizing_variance_with_principal_component_analysis.mp4	32.9 MB
Chapter_13._Strengths_and_weaknesses_of_PCA.mp4	2.8 MB
Chapter_13._Summary.mp4	3.8 MB
Chapter_13._What_is_principal_component_analysis.mp4	28.9 MB
Chapter_14._Building_your_first_UMAP_model.mp4	18.2 MB
Chapter_14._Building_your_first_t-SNE_embedding.mp4	26.4 MB
Chapter_14._Maximizing_similarity_with_t-SNE_and_UMAP.mp4	37.0 MB
Chapter_14._Strengths_and_weaknesses_of_t-SNE_and_UMAP.mp4	3.5 MB
Chapter_14._Summary.mp4	3.3 MB
Chapter_14._What_is_UMAP.mp4	17.3 MB
Chapter_15._Building_an_LLE_of_our_flea_data.mp4	5.8 MB
Chapter_15._Building_your_first_LLE.mp4	19.9 MB
Chapter_15._Building_your_first_SOM.mp4	64.8 MB
Chapter_15._Self-organizing_maps_and_locally_linear_embedding.mp4	13.3 MB
Chapter_15._Strengths_and_weaknesses_of_SOMs_and_LLE.mp4	5.9 MB
Chapter_15._Summary.mp4	4.1 MB
Chapter_15._What_are_self-organizing_maps.mp4	32.6 MB
Chapter_15._What_is_locally_linear_embedding.mp4	12.0 MB
Chapter_16._Building_your_first_k-means_model.mp4	85.9 MB
Chapter_16._Clustering_by_finding_centers_with_k-means.mp4	34.4 MB
Chapter_16._Strengths_and_weaknesses_of_k-means_clustering.mp4	3.6 MB
Chapter_16._Summary.mp4	3.0 MB
Chapter_17._Building_your_first_agglomerative_hierarchical_clustering_model.mp4	59.3 MB
Chapter_17._Hierarchical_clustering.mp4	35.6 MB
Chapter_17._How_stable_are_our_clusters.mp4	12.1 MB
Chapter_17._Strengths_and_weaknesses_of_hierarchical_clustering.mp4	6.3 MB
Chapter_17._Summary.mp4	4.0 MB
Chapter_18._Building_your_first_DBSCAN_model.mp4	73.2 MB
Chapter_18._Building_your_first_OPTICS_model.mp4	10.2 MB
Chapter_18._Clustering_based_on_density_DBSCAN_and_OPTICS.mp4	57.4 MB
Chapter_18._Strengths_and_weaknesses_of_density-based_clustering.mp4	3.8 MB
Chapter_18._Summary.mp4	5.3 MB
Chapter_19._Building_your_first_Gaussian_mixture_model_for_clustering.mp4	21.3 MB
Chapter_19._Clustering_based_on_distributions_with_mixture_modeling.mp4	46.7 MB
Chapter_19._Strengths_and_weaknesses_of_mixture_model_clustering.mp4	4.7 MB
Chapter_19._Summary.mp4	3.9 MB
Chapter_2._Loading_the_tidyverse.mp4	549.7 KB
Chapter_2._Summary.mp4	7.9 MB
Chapter_2._Tidying,_manipulating,_and_plotting_data_with_the_tidyverse.mp4	15.1 MB
Chapter_2._What_the_dplyr_package_is_and_what_it_does.mp4	19.9 MB
Chapter_2._What_the_ggplot2_package_is_and_what_it_does.mp4	16.6 MB
Chapter_2._What_the_purrr_package_is_and_what_it_does.mp4	26.5 MB
Chapter_2._What_the_tibble_package_is_and_what_it_does.mp4	12.8 MB
Chapter_2._What_the_tidyr_package_is_and_what_it_does.mp4	7.8 MB
Chapter_20._Final_notes_and_further_reading.mp4	69.0 MB
Chapter_20._The_last_word.mp4	1.5 MB
Chapter_20._Where_can_you_go_from_here.mp4	23.2 MB
Chapter_3._Balancing_two_sources_of_model_error_The_bias-variance_trade-off.mp4	16.8 MB
Chapter_3._Building_your_first_kNN_model.mp4	27.3 MB
Chapter_3._Classifying_based_on_similarities_with_k-nearest_neighbors.mp4	23.9 MB
Chapter_3._Cross-validating_our_kNN_model.mp4	41.4 MB
Chapter_3._Strengths_and_weaknesses_of_kNN.mp4	5.7 MB
Chapter_3._Summary.mp4	9.8 MB
Chapter_3._Tuning_k_to_improve_the_model.mp4	24.1 MB
Chapter_3._Using_cross-validation_to_tell_if_we_re_overfitting_or_underfitting.mp4	7.0 MB
Chapter_3._What_algorithms_can_learn,_and_what_they_must_be_told_Parameters-_s_and_hyperparameters.mp4	11.2 MB
Chapter_4._Building_your_first_logistic_regression_model.mp4	42.8 MB
Chapter_4._Classifying_based_on_odds_with_logistic_regression.mp4	57.9 MB
Chapter_4._Cross-validating_the_logistic_regression_model.mp4	12.0 MB
Chapter_4._Interpreting_the_model_The_odds_ratio.mp4	12.2 MB
Chapter_4._Strengths_and_weaknesses_of_logistic_regression.mp4	5.2 MB
Chapter_4._Summary.mp4	7.2 MB
Chapter_4._Using_our_model_to_make_predictions.mp4	2.4 MB
Chapter_5._Building_your_first_linear_and_quadratic_discriminant_models.mp4	22.0 MB
Chapter_5._Classifying_by_maximizing_separation_with_discriminant_analysis.mp4	59.5 MB
Chapter_5._Strengths_and_weaknesses_of_LDA_and_QDA.mp4	5.2 MB
Chapter_5._Summary.mp4	5.7 MB
Chapter_6._Building_your_first_SVM_model.mp4	34.6 MB
Chapter_6._Building_your_first_naive_Bayes_model.mp4	17.9 MB
Chapter_6._Classifying_with_naive_Bayes_and_support_vector_machines.mp4	33.5 MB
Chapter_6._Cross-validating_our_SVM_model.mp4	7.4 MB
Chapter_6._Strengths_and_weaknesses_of_naive_Bayes.mp4	2.9 MB
Chapter_6._Strengths_and_weaknesses_of_the_SVM_algorithm.mp4	3.7 MB
Chapter_6._Summary.mp4	6.2 MB
Chapter_6._What_is_the_support_vector_machine_(SVM)_algorithm.mp4	62.3 MB
Chapter_7._Building_your_first_decision_tree_model.mp4	2.9 MB
Chapter_7._Classifying_with_decision_trees.mp4	52.6 MB
Chapter_7._Cross-validating_our_decision_tree_model.mp4	7.7 MB
Chapter_7._Loading_and_exploring_the_zoo_dataset.mp4	3.3 MB
Chapter_7._Strengths_and_weaknesses_of_tree-based_algorithms.mp4	1.9 MB
Chapter_7._Summary.mp4	2.3 MB
Chapter_7._Training_the_decision_tree_model.mp4	31.5 MB
Chapter_8._Benchmarking_algorithms_against_each_other.mp4	7.4 MB
Chapter_8._Building_your_first_XGBoost_model.mp4	22.6 MB
Chapter_8._Building_your_first_random_forest_model.mp4	13.4 MB
Chapter_8._Improving_decision_trees_with_random_forests_and_boosting.mp4	62.6 MB
Chapter_8._Strengths_and_weaknesses_of_tree-based_algorithms.mp4	3.1 MB
Chapter_8._Summary.mp4	3.5 MB
Chapter_9._Building_your_first_linear_regression_model.mp4	126.0 MB
Chapter_9._Linear_regression.mp4	51.5 MB
Chapter_9._Strengths_and_weaknesses_of_linear_regression.mp4	3.3 MB
Chapter_9._Summary.mp4	4.1 MB
Part_1._Introduction.mp4	5.7 MB
Part_2._Classification.mp4	5.6 MB
Part_3._Regression.mp4	4.5 MB
Part_4._Dimension_reduction.mp4	3.8 MB
Part_5._Clustering.mp4	3.1 MB
Total files 137