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Best selling Machine Learning with R, the tidyverse, and mlr notes

Fundamentals_of_Linear_Predictive_Modeling Fundamentals_of_Linear_Predictive_Modeling Popular
  • Fundamentals_of_Linear_Predictive_Modeling

  • Exam (elaborations) • 7 pages • 2023 Popular
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  • Write a fully executed R-Markdown program and submit a pdf / word or html file performing classification task on the Binary response variable from the Santander Bank Case Study. Make sure to try various hyperparameters of the SVM algorithm to find the best available model. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of the Cost Function.
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Fundamentals_of_ensemble_modeling_Week5_NEC Fundamentals_of_ensemble_modeling_Week5_NEC Popular
  • Fundamentals_of_ensemble_modeling_Week5_NEC

  • Exam (elaborations) • 13 pages • 2023 Popular
  • Available in package deal
  • Write a fully executed R-Markdown program and submit a pdf / word or html file performing classification task using Random Forest and XGBoost algorithms on the Binary response variable from the Santander Bank Case Study. Make sure to try various hyperparameter settings of the two algorithms to find the best available models. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of the Cost Function.
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MNIST_Fashion_MNIST_image_data_ML_Week13_NEC MNIST_Fashion_MNIST_image_data_ML_Week13_NEC Popular
  • MNIST_Fashion_MNIST_image_data_ML_Week13_NEC

  • Exam (elaborations) • 89 pages • 2023 Popular
  • Available in package deal
  • The main objective is to write a fully executed R-Markdown program performing clustering using DBSCAN and Mixture model on the MNIST / Fashion MNIST (apparel) images that are 28 x 28 pixels resolution. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of the Cost Function.
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Perform_SVM_on_the_ranking_task_Week4_NEC Perform_SVM_on_the_ranking_task_Week4_NEC
  • Perform_SVM_on_the_ranking_task_Week4_NEC

  • Exam (elaborations) • 6 pages • 2023 Popular
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  • Perform SVM on the ranking task from previous week.
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Fundamentals_of_general_additive_models_ML_Week7_NEC Fundamentals_of_general_additive_models_ML_Week7_NEC
  • Fundamentals_of_general_additive_models_ML_Week7_NEC

  • Exam (elaborations) • 14 pages • 2023 Popular
  • Available in package deal
  • Write a fully executed R-Markdown program and submit a pdf / word or html file performing regression task using GAM algorithms on the primary response variable in the Nutrition Case Study. Make sure to try various hyperparameter settings to find the best available models. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of the Cost Function.
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Classification_with_Logistic_Regression_Week3_NEC Classification_with_Logistic_Regression_Week3_NEC
  • Classification_with_Logistic_Regression_Week3_NEC

  • Exam (elaborations) • 30 pages • 2023 Popular
  • Available in package deal
  • Write a fully executed R-Markdown program and submit a pdf / word or html file performing classification task on the Binary response variable from the Santander Bank Case Study. Make sure to try several permutations of the model before finding the best available model. You are required to clearly display and explain the models that were run for this task.
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Nutrition_Case_Study_ML_Week8_NEC Nutrition_Case_Study_ML_Week8_NEC
  • Nutrition_Case_Study_ML_Week8_NEC

  • Exam (elaborations) • 19 pages • 2023 Popular
  • Available in package deal
  • The main objective is to write a fully executed R-Markdown program performing regression prediction for the response variable using the best models found for LASSO, Ridge and Elastic Net techniques predicting the response variable in the Nutrition case study. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of t...
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Nutrition_Case_Study_ML_Week9_NEC Nutrition_Case_Study_ML_Week9_NEC
  • Nutrition_Case_Study_ML_Week9_NEC

  • Exam (elaborations) • 13 pages • 2023 Popular
  • Available in package deal
  • The main objective is to write a fully executed R-Markdown program performing regression prediction for the response variable using the best models found for kNN, Random Forest and XGBoost techniques predicting the response variable in the Nutrition case study. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of...
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MNIST _Fashion_MNIST_image_data_ML_Wk12_NEC_Solved MNIST _Fashion_MNIST_image_data_ML_Wk12_NEC_Solved
  • MNIST _Fashion_MNIST_image_data_ML_Wk12_NEC_Solved

  • Exam (elaborations) • 38 pages • 2023 Popular
  • Available in package deal
  • The main objective for this week is to write a fully executed R-Markdown program performing clustering using SOM and LLE on the image data containing MNIST (digits) and Fashion MNIST (apparel) images that are 28 x 28 pixels resolution. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes.
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Nutrition_Case_Study_ML_Week10_NEC Nutrition_Case_Study_ML_Week10_NEC
  • Nutrition_Case_Study_ML_Week10_NEC

  • Exam (elaborations) • 4 pages • 2023 Popular
  • Available in package deal
  • Write a formal report on your findings from the last several weeks for the regression problem of the Nutrition Case Study. The main objective is to write a fully executed R-Markdown program performing regression prediction using the best models found for and comparing the cost functions and R-square values. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. Perform EDA on MNIST and Fashion MNIST data and share your findings in the...
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Newest Machine Learning with R, the tidyverse, and mlr summaries

MNIST_Fashion_MNIST_image_data_ML_Week13_NEC MNIST_Fashion_MNIST_image_data_ML_Week13_NEC New
  • MNIST_Fashion_MNIST_image_data_ML_Week13_NEC

  • Exam (elaborations) • 89 pages • 2023 New
  • Available in package deal
  • The main objective is to write a fully executed R-Markdown program performing clustering using DBSCAN and Mixture model on the MNIST / Fashion MNIST (apparel) images that are 28 x 28 pixels resolution. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of the Cost Function.
    (1)
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Fundamentals_of_ensemble_modeling_Week5_NEC Fundamentals_of_ensemble_modeling_Week5_NEC New
  • Fundamentals_of_ensemble_modeling_Week5_NEC

  • Exam (elaborations) • 13 pages • 2023 New
  • Available in package deal
  • Write a fully executed R-Markdown program and submit a pdf / word or html file performing classification task using Random Forest and XGBoost algorithms on the Binary response variable from the Santander Bank Case Study. Make sure to try various hyperparameter settings of the two algorithms to find the best available models. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of the Cost Function.
    (2)
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Nutrition_Case_Study_ML_Week8_NEC Nutrition_Case_Study_ML_Week8_NEC New
  • Nutrition_Case_Study_ML_Week8_NEC

  • Exam (elaborations) • 19 pages • 2023 New
  • Available in package deal
  • The main objective is to write a fully executed R-Markdown program performing regression prediction for the response variable using the best models found for LASSO, Ridge and Elastic Net techniques predicting the response variable in the Nutrition case study. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of t...
    (1)
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MNIST _Fashion_MNIST_image_data_ML_Wk12_NEC_Solved MNIST _Fashion_MNIST_image_data_ML_Wk12_NEC_Solved
  • MNIST _Fashion_MNIST_image_data_ML_Wk12_NEC_Solved

  • Exam (elaborations) • 38 pages • 2023 New
  • Available in package deal
  • The main objective for this week is to write a fully executed R-Markdown program performing clustering using SOM and LLE on the image data containing MNIST (digits) and Fashion MNIST (apparel) images that are 28 x 28 pixels resolution. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes.
    (1)
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Fundamentals_of_general_additive_models_ML_Week7_NEC Fundamentals_of_general_additive_models_ML_Week7_NEC
  • Fundamentals_of_general_additive_models_ML_Week7_NEC

  • Exam (elaborations) • 14 pages • 2023 New
  • Available in package deal
  • Write a fully executed R-Markdown program and submit a pdf / word or html file performing regression task using GAM algorithms on the primary response variable in the Nutrition Case Study. Make sure to try various hyperparameter settings to find the best available models. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of the Cost Function.
    (1)
  • $10.99
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Nutrition_Case_Study_ML_Week9_NEC Nutrition_Case_Study_ML_Week9_NEC
  • Nutrition_Case_Study_ML_Week9_NEC

  • Exam (elaborations) • 13 pages • 2023 New
  • Available in package deal
  • The main objective is to write a fully executed R-Markdown program performing regression prediction for the response variable using the best models found for kNN, Random Forest and XGBoost techniques predicting the response variable in the Nutrition case study. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of...
    (1)
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Classification_with_Logistic_Regression_Week3_NEC Classification_with_Logistic_Regression_Week3_NEC
  • Classification_with_Logistic_Regression_Week3_NEC

  • Exam (elaborations) • 30 pages • 2023 New
  • Available in package deal
  • Write a fully executed R-Markdown program and submit a pdf / word or html file performing classification task on the Binary response variable from the Santander Bank Case Study. Make sure to try several permutations of the model before finding the best available model. You are required to clearly display and explain the models that were run for this task.
    (0)
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Santander_Bank_Case_Study_ML_Week6_NEC Santander_Bank_Case_Study_ML_Week6_NEC
  • Santander_Bank_Case_Study_ML_Week6_NEC

  • Exam (elaborations) • 16 pages • 2023 New
  • Available in package deal
  • Write a formal report on your findings from the last several weeks for the classification of the Santander Bank Case Study. The main objective is to write a fully executed R-Markdown program performing classification using the best models found for logistic regression, SVM, Random Forest and XGBoost algorithms, and comparing the values of their cost functions and accuracy scores. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes. ...
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Human_Activity_Recognition_Case_Analysis_ML_Week14_NEC Human_Activity_Recognition_Case_Analysis_ML_Week14_NEC
  • Human_Activity_Recognition_Case_Analysis_ML_Week14_NEC

  • Exam (elaborations) • 67 pages • 2023 New
  • Available in package deal
  • The main objective is to write a fully executed R-Markdown program performing EDA on the given data on the human activity recognition experiment collected through smart watches.
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Fundamentals_of_Linear_Predictive_Modeling Fundamentals_of_Linear_Predictive_Modeling
  • Fundamentals_of_Linear_Predictive_Modeling

  • Exam (elaborations) • 7 pages • 2023 New
  • Available in package deal
  • Write a fully executed R-Markdown program and submit a pdf / word or html file performing classification task on the Binary response variable from the Santander Bank Case Study. Make sure to try various hyperparameters of the SVM algorithm to find the best available model. You are required to clearly display and explain the models that were run for this task and their effect on the reduction of the Cost Function.
    (0)
  • $10.99
  • 4x sold
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