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Udemy - Case Studies in Data Mining with R

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  • 文件大小:7GB
  • 下载次数:171
  • 最近下载:2021-01-12 18:25:11
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文件列表

  1. 04 Obtaining Prediction Models/002 Creating Prediction Models.mp4 107MB
  2. 04 Obtaining Prediction Models/003 Examine Alternative Regression Models.mp4 105MB
  3. 04 Obtaining Prediction Models/004 Regression Trees.mp4 96MB
  4. 07 Pre-Processing the Data to Apply Methodology/004 Pre-Processing the Data part 3.mp4 92MB
  5. 01 A Brief Introduction to R and RStudio using Scripts/002 Introduction to R for Data Mining.mp4 88MB
  6. 07 Pre-Processing the Data to Apply Methodology/008 Lift Charts and Precision Recall Curves.mp4 87MB
  7. 03 Introduction to Predicting Algae Blooms/009 Imputation Replace Missing Values through Correlation.mp4 86MB
  8. 10 Sidebar on Boosting/005 Boosting Extensions and Variants.mp4 85MB
  9. 01 A Brief Introduction to R and RStudio using Scripts/007 Generating Sequences.mp4 85MB
  10. 10 Sidebar on Boosting/004 Replicating Adaboost using Rpart part 2.mp4 84MB
  11. 07 Pre-Processing the Data to Apply Methodology/005 Defining Data Mining Tasks.mp4 82MB
  12. 03 Introduction to Predicting Algae Blooms/006 Imputation Dealing with Unknown or Missing Values.mp4 80MB
  13. 07 Pre-Processing the Data to Apply Methodology/001 Review the Data and the Focus of the Fraudulent Transactions Case.mp4 79MB
  14. 14 Model Evaluation and Selection/008 Set Up Ranksystems.mp4 78MB
  15. 04 Obtaining Prediction Models/001 Read in Data Files.mp4 78MB
  16. 05 Evaluating and Selecting Models/001 Alternative Model Evaluation Criteria.mp4 76MB
  17. 12 Prediction Tasks and Models/008 Create Initial Model part 2.mp4 76MB
  18. 11 Introduction to Stock Market Prediction Case Study/007 Defining the Prediction Tasks part 2.mp4 75MB
  19. 05 Evaluating and Selecting Models/008 Predicting from the Models.mp4 75MB
  20. 09 The Data Mining Tasks to Find the Fraudulent Transactions/007 Supervised and Unsupervised Approaches.mp4 74MB
  21. 10 Sidebar on Boosting/003 Replicating Adaboost using Rpart Recursive Partitioning Package.mp4 73MB
  22. 05 Evaluating and Selecting Models/003 Setting up K-Fold Evaluation part 1.mp4 72MB
  23. 12 Prediction Tasks and Models/011 Neural Network Prediction Technique part 1.mp4 72MB
  24. 14 Model Evaluation and Selection/002 Begin Evaluating Models.mp4 72MB
  25. 03 Introduction to Predicting Algae Blooms/001 Predicting Algae Blooms.mp4 71MB
  26. 08 Methodology to Find Outliers Fraudulent Transactions/007 Experimental Methodology to find Outliers part 2.mp4 71MB
  27. 11 Introduction to Stock Market Prediction Case Study/002 Case Study Background and Data part 1.mp4 70MB
  28. 01 A Brief Introduction to R and RStudio using Scripts/014 Creating New Functions.mp4 70MB
  29. 15 Wrap Up Stock Market Case Study/001 Prologue to Last Session Wrap-Up.mp4 69MB
  30. 11 Introduction to Stock Market Prediction Case Study/003 Case Study Background and Data part 2.mp4 68MB
  31. 09 The Data Mining Tasks to Find the Fraudulent Transactions/005 Local Outlier Factors.mp4 68MB
  32. 08 Methodology to Find Outliers Fraudulent Transactions/008 Experimental Methodology to find Outliers part 3.mp4 67MB
  33. 05 Evaluating and Selecting Models/009 Comparing the Predictions.mp4 67MB
  34. 05 Evaluating and Selecting Models/002 Introduction to K-Fold Cross-Validation.mp4 66MB
  35. 05 Evaluating and Selecting Models/007 Finish Evaluating Models.mp4 66MB
  36. 03 Introduction to Predicting Algae Blooms/008 Imputation Replace Missing Values with Central Measures.mp4 66MB
  37. 04 Obtaining Prediction Models/005 Strategy for Pruning Trees.mp4 65MB
  38. 12 Prediction Tasks and Models/012 Neural Network Prediction Technique part 2.mp4 65MB
  39. 12 Prediction Tasks and Models/004 Decision Trees part 3.mp4 64MB
  40. 12 Prediction Tasks and Models/007 Create Initial Model part 1.mp4 64MB
  41. 08 Methodology to Find Outliers Fraudulent Transactions/009 Experimental Methodology to find Outliers part 4.mp4 64MB
  42. 06 Examine the Data in the Fraudulent Transactions Case Study/004 Exploring the Data with Eye toward Missingness.mp4 64MB
  43. 03 Introduction to Predicting Algae Blooms/002 Visualizing other Imputations with Lattice Plots.mp4 64MB
  44. 03 Introduction to Predicting Algae Blooms/003 Data Visualization and Summarization Histograms.mp4 63MB
  45. 11 Introduction to Stock Market Prediction Case Study/006 Defining the Prediction Tasks part 1.mp4 63MB
  46. 07 Pre-Processing the Data to Apply Methodology/002 Pre-Processing the Data part 1.mp4 63MB
  47. 14 Model Evaluation and Selection/007 Experimental Model Comparisons part 2.mp4 63MB
  48. 14 Model Evaluation and Selection/010 Continue Evaluating part 2.mp4 63MB
  49. 01 A Brief Introduction to R and RStudio using Scripts/011 Data Structures Lists.mp4 62MB
  50. 09 The Data Mining Tasks to Find the Fraudulent Transactions/008 SMOTE and Naive Bayes part 1.mp4 61MB
  51. 12 Prediction Tasks and Models/003 Decision Trees part 2.mp4 61MB
  52. 03 Introduction to Predicting Algae Blooms/005 Data Visualization Conditioning Plots.mp4 61MB
  53. 15 Wrap Up Stock Market Case Study/002 Last Session Wrap-Up part 1.mp4 60MB
  54. 11 Introduction to Stock Market Prediction Case Study/008 Defining the Prediction Tasks part 3.mp4 60MB
  55. 02 Inputting and Outputting Data and Text/008 Reading and Writing Files part 2.mp4 59MB
  56. 09 The Data Mining Tasks to Find the Fraudulent Transactions/001 Review of Fraud Case part 1.mp4 59MB
  57. 02 Inputting and Outputting Data and Text/004 Using readLines Function and Text Data.mp4 58MB
  58. 08 Methodology to Find Outliers Fraudulent Transactions/006 Experimental Methodology to find Outliers part 1.mp4 57MB
  59. 03 Introduction to Predicting Algae Blooms/007 Imputation Removing Rows with Missing Values.mp4 57MB
  60. 14 Model Evaluation and Selection/006 Experimental Model Comparisons part 1.mp4 57MB
  61. 01 A Brief Introduction to R and RStudio using Scripts/013 Data Structures Dataframes part 2.mp4 57MB
  62. 09 The Data Mining Tasks to Find the Fraudulent Transactions/002 Review of Fraud Case part 2.mp4 57MB
  63. 07 Pre-Processing the Data to Apply Methodology/003 Pre-Processing the Data part 2.mp4 56MB
  64. 14 Model Evaluation and Selection/001 Quick Review of Case Study Support Vector Machines SVMs.mp4 56MB
  65. 14 Model Evaluation and Selection/009 Continue Evaluating part 1.mp4 56MB
  66. 05 Evaluating and Selecting Models/006 Best Model part 2.mp4 56MB
  67. 09 The Data Mining Tasks to Find the Fraudulent Transactions/003 Review of Fraud Case part 3.mp4 55MB
  68. 05 Evaluating and Selecting Models/004 Setting up K-Fold Evaluation part 2.mp4 55MB
  69. 07 Pre-Processing the Data to Apply Methodology/007 Precision and Recall.mp4 55MB
  70. 14 Model Evaluation and Selection/011 Continue Evaluating part 3.mp4 54MB
  71. 10 Sidebar on Boosting/001 Introduction to Boosting from Rattle course.mp4 54MB
  72. 11 Introduction to Stock Market Prediction Case Study/004 Accessing the Data part 1.mp4 53MB
  73. 08 Methodology to Find Outliers Fraudulent Transactions/004 Cumulative Recall Chart.mp4 52MB
  74. 01 A Brief Introduction to R and RStudio using Scripts/006 Factors part 2.mp4 52MB
  75. 10 Sidebar on Boosting/002 Boosting Demo Basics using R.mp4 52MB
  76. 09 The Data Mining Tasks to Find the Fraudulent Transactions/009 SMOTE and Naive Bayes part 2.mp4 52MB
  77. 13 Prediction Models and Support Vector Machines SVMs/004 SVMs Applied to Stock Market Case.mp4 51MB
  78. 13 Prediction Models and Support Vector Machines SVMs/006 Multivariate Adaptive Regressive Splines.mp4 51MB
  79. 13 Prediction Models and Support Vector Machines SVMs/009 Writing a Simulated Trader Function part 1.mp4 51MB
  80. 15 Wrap Up Stock Market Case Study/003 Last Session Wrap-Up part 2.mp4 50MB
  81. 09 The Data Mining Tasks to Find the Fraudulent Transactions/006 Plotting Everything.mp4 50MB
  82. 13 Prediction Models and Support Vector Machines SVMs/007 How Will the Predictions be Used .mp4 50MB
  83. 01 A Brief Introduction to R and RStudio using Scripts/012 Data Structures Dataframes part 1.mp4 49MB
  84. 08 Methodology to Find Outliers Fraudulent Transactions/003 Review Lift Charts and Precision Recall Curves.mp4 49MB
  85. 06 Examine the Data in the Fraudulent Transactions Case Study/005 Continue Exploring the Data.mp4 49MB
  86. 14 Model Evaluation and Selection/003 Evaluating Policy One and Policy Two.mp4 49MB
  87. 02 Inputting and Outputting Data and Text/005 Example Program powers.R.mp4 48MB
  88. 02 Inputting and Outputting Data and Text/006 Example Program quad2b.R.mp4 48MB
  89. 08 Methodology to Find Outliers Fraudulent Transactions/002 Review Precision and Recall.mp4 48MB
  90. 03 Introduction to Predicting Algae Blooms/004 Data Visualization Boxplot and Identity Plot.mp4 48MB
  91. 12 Prediction Tasks and Models/010 Precision and Recall and Confusion Matrices.mp4 48MB
  92. 07 Pre-Processing the Data to Apply Methodology/006 Semi-Supervised Techniques.mp4 48MB
  93. 01 A Brief Introduction to R and RStudio using Scripts/004 Data Structures Vectors part 2.mp4 48MB
  94. 13 Prediction Models and Support Vector Machines SVMs/002 Review Support Vector Machines SVMs using Weather Data part 2.mp4 48MB
  95. 14 Model Evaluation and Selection/005 So What Approach is Recommended .mp4 48MB
  96. 13 Prediction Models and Support Vector Machines SVMs/008 Two Strategies.mp4 47MB
  97. 12 Prediction Tasks and Models/002 Decision Trees as Applicable to Case Study Tasks.mp4 47MB
  98. 12 Prediction Tasks and Models/005 Decision Trees part 4.mp4 47MB
  99. 12 Prediction Tasks and Models/009 The Prediction Tasks.mp4 46MB
  100. 13 Prediction Models and Support Vector Machines SVMs/011 Evaluating our Simulated Trades.mp4 46MB
  101. 12 Prediction Tasks and Models/006 Random Forests Review.mp4 45MB
  102. 10 Sidebar on Boosting/006 Boosting Exercise.mp4 45MB
  103. 14 Model Evaluation and Selection/004 Why You Cannot Randomly Resample Records.mp4 45MB
  104. 05 Evaluating and Selecting Models/005 Best Model part 1.mp4 44MB
  105. 02 Inputting and Outputting Data and Text/003 Using readline, cat and print Functions.mp4 44MB
  106. 01 A Brief Introduction to R and RStudio using Scripts/003 Data Structures Vectors part 1.mp4 44MB
  107. 11 Introduction to Stock Market Prediction Case Study/009 Defining the Prediction Tasks part 4.mp4 44MB
  108. 13 Prediction Models and Support Vector Machines SVMs/001 Review Support Vector Machines SVMs using Weather Data part 1.mp4 43MB
  109. 11 Introduction to Stock Market Prediction Case Study/005 Accessing the Data part 2.mp4 43MB
  110. 01 A Brief Introduction to R and RStudio using Scripts/009 Data Structures Matrices and Arrays part 1.mp4 43MB
  111. 11 Introduction to Stock Market Prediction Case Study/010 Defining the Prediction Tasks part 5.mp4 43MB
  112. 01 A Brief Introduction to R and RStudio using Scripts/008 Indexing aka Subscripting or Subsetting.mp4 41MB
  113. 01 A Brief Introduction to R and RStudio using Scripts/005 Factors part 1.mp4 41MB
  114. 13 Prediction Models and Support Vector Machines SVMs/010 Writing a Simulated Trader Function part 2.mp4 41MB
  115. 13 Prediction Models and Support Vector Machines SVMs/005 Kernel Functions.mp4 41MB
  116. 01 A Brief Introduction to R and RStudio using Scripts/010 Data Structures Matrices and Arrays part 2.mp4 39MB
  117. 09 The Data Mining Tasks to Find the Fraudulent Transactions/004 Baseline Boxplot Rule.mp4 39MB
  118. 08 Methodology to Find Outliers Fraudulent Transactions/005 Creating More Functions for the Experimental Methodology.mp4 38MB
  119. 13 Prediction Models and Support Vector Machines SVMs/003 Review Support Vector Machines SVMs using Weather Data part 3.mp4 36MB
  120. 08 Methodology to Find Outliers Fraudulent Transactions/010 Experimental Methodology to find Outliers part 5.mp4 33MB
  121. 02 Inputting and Outputting Data and Text/001 Using the scan Function for Input part 1.mp4 25MB
  122. 02 Inputting and Outputting Data and Text/002 Using the scan Function for Input part 2.mp4 24MB
  123. 02 Inputting and Outputting Data and Text/007 Reading and Writing Files part 1.mp4 23MB
  124. 06 Examine the Data in the Fraudulent Transactions Case Study/001 Exercise Solution from Evaluating and Selecting Models.mp4 20MB
  125. 06 Examine the Data in the Fraudulent Transactions Case Study/003 Prelude to Exploring the Data.mp4 19MB
  126. 12 Prediction Tasks and Models/001 Prelude to Modeling Stock Market Indices.mp4 19MB
  127. 11 Introduction to Stock Market Prediction Case Study/001 Introduction to Stock Market Case Study and Materials.mp4 15MB
  128. 08 Methodology to Find Outliers Fraudulent Transactions/001 Exercise from Previous Session.mp4 13MB
  129. 06 Examine the Data in the Fraudulent Transactions Case Study/002 Fraudulent Case Study Introduction.mp4 11MB
  130. 01 A Brief Introduction to R and RStudio using Scripts/001 Course Overview.mp4 8MB