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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

Description:

COURSE OUTLINE: Data mining is the study of algorithms for finding patterns in large data sets. It is an integral part of modern industry, where data from its operations and customers are mined for gaining business insight. It is also important in modern scientific endeavors. Data mining is an interdisciplinary topic involving, databases, machine learning and algorithms. The course will cover the fundamentals of data mining. It will explain the basic algorithms like data preprocessing, association rules, classification, clustering, sequence mining and visualization. It will also explain implementations in open-source software. Finally, case studies on industrial problems will be demonstrated.

Data Mining

NPTEL

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#Data Science #Data Mining #Computer Science #Artificial Intelligence #Neural Networks #Classification #Machine Learning #Decision Trees #Data Preprocessing #K-Nearest Neighbors

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