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[] [UDEMY] Beginner to Advanced Guide on Machine Learning with R Tool [FTU]

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  • 文件大小:339MB
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文件列表

  1. 7. Module-7 Regression/7. 7.7 Implementation of Forecasting.mp4 38MB
  2. 3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.mp4 34MB
  3. 7. Module-7 Regression/6. 7.6 Forecasting.mp4 20MB
  4. 1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.mp4 18MB
  5. 2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.mp4 16MB
  6. 3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.mp4 15MB
  7. 7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.mp4 12MB
  8. 4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.mp4 12MB
  9. 6. Module-6 Clustering/2. 6.2 K-Means Clustering.mp4 11MB
  10. 5. Module-5 Tree Based Models/4. 5.4 Boosting.mp4 11MB
  11. 7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.mp4 10MB
  12. 4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.mp4 9MB
  13. 5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.mp4 9MB
  14. 6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.mp4 8MB
  15. 5. Module-5 Tree Based Models/3. 5.3 Bagging.mp4 8MB
  16. 5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.mp4 7MB
  17. 6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.mp4 7MB
  18. 7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.mp4 7MB
  19. 3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.mp4 6MB
  20. 3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.mp4 6MB
  21. 1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.mp4 6MB
  22. 2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.mp4 5MB
  23. 4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.mp4 5MB
  24. 3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.mp4 5MB
  25. 4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.mp4 5MB
  26. 5. Module-5 Tree Based Models/1. 5.1 Decision Tree.mp4 5MB
  27. 7. Module-7 Regression/4. 7.4 Logistic Regression.mp4 5MB
  28. 7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.mp4 5MB
  29. 4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.mp4 4MB
  30. 5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.mp4 4MB
  31. 1. Module-1 Introduction to Course/3. 1.3 What you will Learn.mp4 4MB
  32. 1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.mp4 4MB
  33. 2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.mp4 3MB
  34. 3. Module-3 Classification/1. 3.1 Introduction to Classification.mp4 3MB
  35. 4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.mp4 3MB
  36. 6. Module-6 Clustering/1. 6.1 Introduction to Clustering.mp4 3MB
  37. 4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.mp4 3MB
  38. 3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.mp4 2MB
  39. 3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.vtt 15KB
  40. 2. Module-2 Introduction to validation and its Methods/3.1 Programs.zip.zip 11KB
  41. 3. Module-3 Classification/3.1 Programs.zip.zip 11KB
  42. 3. Module-3 Classification/5.1 Programs.zip.zip 11KB
  43. 3. Module-3 Classification/7.1 Programs.zip.zip 11KB
  44. 4. Module-4 Black Box Method-Neural network and SVM/3.1 Programs.zip.zip 11KB
  45. 4. Module-4 Black Box Method-Neural network and SVM/5.1 Programs.zip.zip 11KB
  46. 4. Module-4 Black Box Method-Neural network and SVM/7.1 Programs.zip.zip 11KB
  47. 5. Module-5 Tree Based Models/2.1 Programs.zip.zip 11KB
  48. 5. Module-5 Tree Based Models/3.1 Programs.zip.zip 11KB
  49. 5. Module-5 Tree Based Models/4.1 Programs.zip.zip 11KB
  50. 5. Module-5 Tree Based Models/6.1 Programs.zip.zip 11KB
  51. 6. Module-6 Clustering/3.1 Programs.zip.zip 11KB
  52. 6. Module-6 Clustering/4.1 Programs.zip.zip 11KB
  53. 7. Module-7 Regression/2.1 Programs.zip.zip 11KB
  54. 7. Module-7 Regression/3.1 Programs.zip.zip 11KB
  55. 7. Module-7 Regression/5.1 Programs.zip.zip 11KB
  56. 7. Module-7 Regression/7.1 Programs.zip.zip 11KB
  57. 2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.vtt 8KB
  58. 6. Module-6 Clustering/2. 6.2 K-Means Clustering.vtt 8KB
  59. 3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.vtt 7KB
  60. 5. Module-5 Tree Based Models/4. 5.4 Boosting.vtt 6KB
  61. 7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.vtt 6KB
  62. 7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.vtt 5KB
  63. 4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.vtt 5KB
  64. 1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.vtt 4KB
  65. 4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.vtt 4KB
  66. 5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.vtt 4KB
  67. 3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.vtt 4KB
  68. 2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.vtt 4KB
  69. 5. Module-5 Tree Based Models/3. 5.3 Bagging.vtt 4KB
  70. 6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.vtt 3KB
  71. 5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.vtt 3KB
  72. 6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.vtt 3KB
  73. 7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.vtt 3KB
  74. 3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.vtt 3KB
  75. 3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.vtt 3KB
  76. 7. Module-7 Regression/6. 7.6 Forecasting.vtt 3KB
  77. 4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.vtt 3KB
  78. 7. Module-7 Regression/4. 7.4 Logistic Regression.vtt 3KB
  79. 7. Module-7 Regression/7. 7.7 Implementation of Forecasting.vtt 3KB
  80. 5. Module-5 Tree Based Models/1. 5.1 Decision Tree.vtt 3KB
  81. 7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.vtt 3KB
  82. 1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.vtt 3KB
  83. 4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.vtt 2KB
  84. 5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.vtt 2KB
  85. 2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.vtt 2KB
  86. 1. Module-1 Introduction to Course/3. 1.3 What you will Learn.vtt 2KB
  87. 3. Module-3 Classification/1. 3.1 Introduction to Classification.vtt 2KB
  88. 6. Module-6 Clustering/1. 6.1 Introduction to Clustering.vtt 2KB
  89. 4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.vtt 2KB
  90. 4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.vtt 2KB
  91. 4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.vtt 2KB
  92. 3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.vtt 1KB
  93. 1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.vtt 776B
  94. 0. Websites you may like/1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url 328B
  95. 0. Websites you may like/5. (Discuss.FTUForum.com) FTU Discussion Forum.url 294B
  96. 0. Websites you may like/2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url 286B
  97. 0. Websites you may like/4. (FTUApps.com) Download Cracked Developers Applications For Free.url 239B
  98. 0. Websites you may like/How you can help Team-FTU.txt 237B
  99. 0. Websites you may like/3. (NulledPremium.com) Download Cracked Website Themes, Plugins, Scripts And Stock Images.url 163B