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
Lecture 37 - Regression - I
Lecture 38 - Regression - II
Lecture 39 - Regression - III
Lecture 40 - Regression - IV
Lecture 41 - Dimensionality Reduction - I
Lecture 42 - Dimensionality Reduction - II
Lecture 43 - Tutorial
Lecture 44 - Live Session
