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