• Lesson.No : 26
  • 00:28:11

  • Lecture 26 : Support Vector Machine V

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Course Lessons

  1. 1- Lecture 1 Introduction, Knowledge Discovery Process
  2. 2- Lecture 2 Data Preprocessing - I
  3. 3- Lecture 3 Data Preprocessing - II
  4. 4- Lecture 4 Association Rules
  5. 5- Lecture 5 Apriori algorithm
  6. 6- Lecture 6 : Rule generation
  7. 7- Lecture 7 : Classification
  8. 8- Lecture 8 : Decision Tree - I
  9. 9- Lecture 9 : Decision Tree - II
  10. 10- Lecture 10 : Decision Tree III
  11. 11- Lecture 11 : Decision Tree IV
  12. 12- Lecture 12 : Bayes Classifier I
  13. 13- Lecture 13 : Bayes Classifier II
  14. 14- Lecture 14 : Bayes Classifier III
  15. 15- Lecture 15 : Bayes Classifier IV
  16. 16- Lecture 16 : Bayes Classifier V
  17. 17- Lecture 17 : K Nearest Neighbor I
  18. 18- Lecture 18 : K Nearest Neighbor II
  19. 19- Lecture 19 :
  20. 20- Lecture 20
  21. 21- Lecture 21
  22. 22- Lecture 22 : Support Vector Machine I
  23. 23- Lecture 23 : Support Vector Machine II
  24. 24- Lecture 24 : Support Vector Machine III
  25. 25- Lecture 25 : Support Vector Machine IV
  26. 26- Lecture 26 : Support Vector Machine V
  27. 27- Lecture 27: Kernel Machines
  28. 28- Lecture 28: Artificial Neural Networks I
  29. 29- Lecture 29:Artificial Neural Networks II
  30. 30- Lecture 30: Artificial Neural Networks III
  31. 31- Lecture 31: Artificial Neural Networks IV
  32. 32- Lecture 32: Clustering I
  33. 33- Lecture 33: Clustering II
  34. 34- Lecture 34: Clustering III
  35. 35- Lecture 35: Clustering IV
  36. 36- Lecture 36: Clustering V