Machine Learning with R, the tidyverse, and mlr Video Edition --> [ FreeCourseWeb ]

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Torrent File Content (137 files)

[ FreeCourseWeb.com ] Machine Learning with R, the tidyverse, and mlr. Video Edition
Get Bonus Downloads Here.url -

183 bytes

~Get Your Files Here !
Appendix._Central_tendency.mp4 -

10.21 MB

Appendix._Distributions.mp4 -

9.72 MB

Appendix._Logarithms.mp4 -

9.25 MB

Appendix._Measures_of_dispersion.mp4 -

21.24 MB

Appendix._Measures_of_the_relationships_between_variables.mp4 -

10.66 MB

Appendix._Refresher_on_statistical_concepts.mp4 -

17.37 MB

Appendix._Sigma_notation.mp4 -

5.31 MB

Appendix.__Vectors.mp4 -

5.84 MB

Bonus Resources.txt -

386 bytes

Chapter_1._Classes_of_machine_learning_algorithms.mp4 -

40.47 MB

Chapter_1._Introduction_to_machine_learning.mp4 -

34.12 MB

Chapter_1._Summary.mp4 -

5.43 MB

Chapter_1._Thinking_about_the_ethical_impact_of_machine_learning.mp4 -

20.42 MB

Chapter_1._What_will_you_learn_in_this_book.mp4 -

2.64 MB

Chapter_1._Which_datasets_will_we_use.mp4 -

1.98 MB

Chapter_1._Why_use_R_for_machine_learning.mp4 -

7.95 MB

Chapter_10._Building_your_first_GAM.mp4 -

19.1 MB

Chapter_10._More_flexibility_Splines_and_generalized_additive_models.mp4 -

20.74 MB

Chapter_10._Strengths_and_weaknesses_of_GAMs.mp4 -

3.79 MB

Chapter_10._Summary.mp4 -

2.55 MB

Chapter_10.__Nonlinear_regression_with_generalized_additive_models.mp4 -

18.48 MB

Chapter_11._Benchmarking_ridge,_LASSO,_elastic_net,_and_OLS_against_each_other.mp4 -

7.29 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 -

4.91 MB

Chapter_11._Summary.mp4 -

4.79 MB

Chapter_11._What_is_elastic_net.mp4 -

11.22 MB

Chapter_11._What_is_ridge_regression.mp4 -

18.48 MB

Chapter_11._What_is_the_L1_norm,_and_how_does_LASSO_use_it.mp4 -

8.28 MB

Chapter_11._What_is_the_L2_norm,_and_how_does_ridge_regression_use_it.mp4 -

18.62 MB

Chapter_12._Benchmarking_the_kNN,_random_forest,_and_XGBoost_model-building_processes.mp4 -

4.38 MB

Chapter_12._Building_your_first_XGBoost_regression_model.mp4 -

12.23 MB

Chapter_12._Building_your_first_kNN_regression_model.mp4 -

32.3 MB

Chapter_12._Building_your_first_random_forest_regression_model.mp4 -

9.84 MB

Chapter_12._Regression_with_kNN,_random_forest,_and_XGBoost.mp4 -

14.13 MB

Chapter_12._Strengths_and_weaknesses_of_kNN,_random_forest,_and_XGBoost.mp4 -

2.55 MB

Chapter_12._Summary.mp4 -

3.68 MB

Chapter_12._Using_tree-based_learners_to_predict_a_continuous_variable.mp4 -

12.35 MB

Chapter_13._Building_your_first_PCA_model.mp4 -

43.61 MB

Chapter_13._Maximizing_variance_with_principal_component_analysis.mp4 -

31.4 MB

Chapter_13._Strengths_and_weaknesses_of_PCA.mp4 -

2.72 MB

Chapter_13._Summary.mp4 -

3.67 MB

Chapter_13._What_is_principal_component_analysis.mp4 -

27.53 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 -

35.25 MB

Chapter_14._Strengths_and_weaknesses_of_t-SNE_and_UMAP.mp4 -

3.36 MB

Chapter_14._Summary.mp4 -

3.16 MB

Chapter_14._What_is_UMAP.mp4 -

16.46 MB

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