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 free videos and free material uploaded by Indian Institute of Technology, Kharagpur (IIT Kharagpur). This session contains about Data Mining Updated syllabus , Lecture notes , videos , MCQ , Privious Question papers and Toppers Training Provided Training of this course. If Material not uploaded check another subject
Syllabus / What will i learn?
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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