589689.xyz

Lynda - Machine Learning and AI Foundations - Clustering and Association

  • 收录时间:2018-05-31 08:13:39
  • 文件大小:532MB
  • 下载次数:98
  • 最近下载:2021-01-03 04:43:04
  • 磁力链接:
立即下载 复制链接

文件列表

  1. Exercise Files/Ex_Files_Machine_Learning_AI_Clustering.zip 25MB
  2. 6.5. Anomaly Detection/34.Using SOM for anomaly detection.mp4 22MB
  3. 6.5. Anomaly Detection/32.The k = 1 trick.mp4 21MB
  4. 5.4. Cluster Methods for Categorical Variables/31.A self organizing map example.mp4 20MB
  5. 4.3. Visualizing and Reporting Cluster Solutions/24.Line graphs.mp4 19MB
  6. 2.1. What Is Cluster Analysis/05.Looking at the data with a 2D scatter plot.mp4 19MB
  7. 7.6. Association Rules and Sequence Detection/36.Running association rules.mp4 19MB
  8. 2.1. What Is Cluster Analysis/06.Understanding hierarchical cluster analysis.mp4 19MB
  9. 7.6. Association Rules and Sequence Detection/38.Interpreting association rules.mp4 18MB
  10. 5.4. Cluster Methods for Categorical Variables/25.Relating clusters to categories statistically.mp4 18MB
  11. 5.4. Cluster Methods for Categorical Variables/27.Running a multiple correspondence analysis.mp4 17MB
  12. 2.1. What Is Cluster Analysis/09.Methods for measuring distance.mp4 16MB
  13. 7.6. Association Rules and Sequence Detection/39.Putting association rules to use.mp4 16MB
  14. 6.5. Anomaly Detection/33.Anomaly detection algorithms.mp4 14MB
  15. 7.6. Association Rules and Sequence Detection/41.Sequence detection.mp4 14MB
  16. 3.2. K-Means/15.Interpreting cluster analysis output.mp4 14MB
  17. 4.3. Visualizing and Reporting Cluster Solutions/22.Summarizing cluster means in a table.mp4 14MB
  18. 5.4. Cluster Methods for Categorical Variables/29.Using cluster analysis and decision trees together.mp4 14MB
  19. 3.2. K-Means/19.Finding optimum value for k - k = 4.mp4 14MB
  20. 5.4. Cluster Methods for Categorical Variables/30.A BIRCH_two-step example.mp4 13MB
  21. 3.2. K-Means/18.Finding optimum value for k - k = 3.mp4 13MB
  22. 3.2. K-Means/20.Finding optimum value for k - k = 5.mp4 12MB
  23. 2.1. What Is Cluster Analysis/10.What is k-nearest neighbors.mp4 12MB
  24. 3.2. K-Means/17.Which cases should be used with k-means.mp4 11MB
  25. 5.4. Cluster Methods for Categorical Variables/28.Interpreting a perceptual map.mp4 11MB
  26. 2.1. What Is Cluster Analysis/07.Running hierarchical cluster analysis.mp4 11MB
  27. 2.1. What Is Cluster Analysis/08.Interpreting a dendrogram.mp4 10MB
  28. 3.2. K-Means/14.Running a k-means cluster analysis.mp4 10MB
  29. 3.2. K-Means/21.What the best solution.mp4 10MB
  30. 1.Introduction/04.What is unsupervised machine learning.mp4 9MB
  31. 4.3. Visualizing and Reporting Cluster Solutions/23.Traffic Light feature in Excel.mp4 9MB
  32. 3.2. K-Means/13.Interpreting a box plot.mp4 9MB
  33. 3.2. K-Means/12.Which variables should be used with k-means.mp4 9MB
  34. 7.6. Association Rules and Sequence Detection/35.Intro to association rules and sequence analysis.mp4 7MB
  35. 5.4. Cluster Methods for Categorical Variables/26.Relating clusters to categories visually.mp4 7MB
  36. 3.2. K-Means/11.How does k-means work.mp4 6MB
  37. 1.Introduction/01.Welcome.mp4 6MB
  38. 7.6. Association Rules and Sequence Detection/40.Comparing clustering and association rules.mp4 6MB
  39. 7.6. Association Rules and Sequence Detection/37.Some association rules terminology.mp4 4MB
  40. 1.Introduction/03.Using the exercise files.mp4 4MB
  41. 1.Introduction/02.What you should know.mp4 3MB
  42. 3.2. K-Means/16.What does silhouette mean.mp4 3MB
  43. 8.Conclusion/42.Next steps.mp4 2MB
  44. 5.4. Cluster Methods for Categorical Variables/29.Using cluster analysis and decision trees together.en.srt 15KB
  45. 4.3. Visualizing and Reporting Cluster Solutions/24.Line graphs.en.srt 12KB
  46. 7.6. Association Rules and Sequence Detection/38.Interpreting association rules.en.srt 11KB
  47. 6.5. Anomaly Detection/32.The k = 1 trick.en.srt 11KB
  48. 3.2. K-Means/13.Interpreting a box plot.en.srt 11KB
  49. 5.4. Cluster Methods for Categorical Variables/31.A self organizing map example.en.srt 11KB
  50. 5.4. Cluster Methods for Categorical Variables/25.Relating clusters to categories statistically.en.srt 11KB
  51. 6.5. Anomaly Detection/34.Using SOM for anomaly detection.en.srt 10KB
  52. 3.2. K-Means/19.Finding optimum value for k - k = 4.en.srt 10KB
  53. 1.Introduction/04.What is unsupervised machine learning.en.srt 9KB
  54. 3.2. K-Means/15.Interpreting cluster analysis output.en.srt 9KB
  55. 2.1. What Is Cluster Analysis/05.Looking at the data with a 2D scatter plot.en.srt 9KB
  56. 7.6. Association Rules and Sequence Detection/36.Running association rules.en.srt 9KB
  57. 2.1. What Is Cluster Analysis/09.Methods for measuring distance.en.srt 9KB
  58. 7.6. Association Rules and Sequence Detection/41.Sequence detection.en.srt 9KB
  59. 5.4. Cluster Methods for Categorical Variables/27.Running a multiple correspondence analysis.en.srt 9KB
  60. 2.1. What Is Cluster Analysis/10.What is k-nearest neighbors.en.srt 8KB
  61. 2.1. What Is Cluster Analysis/06.Understanding hierarchical cluster analysis.en.srt 8KB
  62. 3.2. K-Means/18.Finding optimum value for k - k = 3.en.srt 8KB
  63. 4.3. Visualizing and Reporting Cluster Solutions/22.Summarizing cluster means in a table.en.srt 8KB
  64. 7.6. Association Rules and Sequence Detection/35.Intro to association rules and sequence analysis.en.srt 8KB
  65. 3.2. K-Means/20.Finding optimum value for k - k = 5.en.srt 8KB
  66. 7.6. Association Rules and Sequence Detection/39.Putting association rules to use.en.srt 8KB
  67. 3.2. K-Means/17.Which cases should be used with k-means.en.srt 8KB
  68. 5.4. Cluster Methods for Categorical Variables/30.A BIRCH_two-step example.en.srt 8KB
  69. 6.5. Anomaly Detection/33.Anomaly detection algorithms.en.srt 7KB
  70. 2.1. What Is Cluster Analysis/07.Running hierarchical cluster analysis.en.srt 7KB
  71. 2.1. What Is Cluster Analysis/08.Interpreting a dendrogram.en.srt 6KB
  72. 4.3. Visualizing and Reporting Cluster Solutions/23.Traffic Light feature in Excel.en.srt 6KB
  73. 3.2. K-Means/21.What the best solution.en.srt 6KB
  74. 3.2. K-Means/14.Running a k-means cluster analysis.en.srt 5KB
  75. 7.6. Association Rules and Sequence Detection/37.Some association rules terminology.en.srt 5KB
  76. 3.2. K-Means/12.Which variables should be used with k-means.en.srt 5KB
  77. 5.4. Cluster Methods for Categorical Variables/28.Interpreting a perceptual map.en.srt 5KB
  78. 5.4. Cluster Methods for Categorical Variables/26.Relating clusters to categories visually.en.srt 5KB
  79. 7.6. Association Rules and Sequence Detection/40.Comparing clustering and association rules.en.srt 5KB
  80. 1.Introduction/02.What you should know.en.srt 4KB
  81. 3.2. K-Means/16.What does silhouette mean.en.srt 4KB
  82. 3.2. K-Means/11.How does k-means work.en.srt 3KB
  83. 8.Conclusion/42.Next steps.en.srt 3KB
  84. 1.Introduction/03.Using the exercise files.en.srt 2KB
  85. 1.Introduction/01.Welcome.en.srt 1KB