Data Science Foundations Data Mining

2016
4:41 hours

├── 001 Welcome.mp4
├── 002 Who should watch this course.mp4
├── 003 Exercise files.mp4
├── 004 Data mining prerequisites.mp4
├── 005 Algorithm prerequisites.mp4
├── 006 Software prerequisites.mp4
├── 007 Goals of data reduction.mp4
├── 008 Data for data reduction.mp4
├── 009 Data reduction in R.mp4
├── 010 Data reduction in Python.mp4
├── 011 Data reduction in Orange.mp4
├── 012 Data reduction in RapidMiner.mp4
├── 013 Clustering goals.mp4
├── 014 Clustering data.mp4
├── 015 Clustering in R.mp4
├── 016 Clustering in Python.mp4
├── 017 Clustering in BigML.mp4
├── 018 Clustering in Orange.mp4
├── 019 Classification goals.mp4
├── 020 Classification data.mp4
├── 021 Classification in R.mp4
├── 022 Classification in Python.mp4
├── 023 Classification in RapidMiner.mp4
├── 024 Classification in KNIME.mp4
├── 025 Anomaly detection goals.mp4
├── 026 Anomaly detection data.mp4
├── 027 Anomaly detection in R.mp4
├── 028 Anomaly detection in Python.mp4
├── 029 Anomaly detection in BigML.mp4
├── 030 Anomaly detection in RapidMiner.mp4
├── 031 Association analysis goals.mp4
├── 032 Association analysis data.mp4
├── 033 Association analysis in R.mp4
├── 034 Association analysis in Python.mp4
├── 035 Association analysis in Orange.mp4
├── 036 Association analysis in KNIME.mp4
├── 037 Regression analysis goals.mp4
├── 038 Regression analysis data.mp4
├── 039 Regression analysis in R.mp4
├── 040 Regression analysis in Python.mp4
├── 041 Regression analysis in KNIME.mp4
├── 042 Regression analysis in RapidMiner.mp4
├── 043 Sequence mining goals.mp4
├── 044 Sequence mining algorithms.mp4
├── 045 Sequence mining in R.mp4
├── 046 Sequence mining in Python.mp4
├── 047 Sequence mining in BigML – Part 1.mp4
├── 048 Sequence mining in BigML – Part 2.mp4
├── 049 Text mining goals.mp4
├── 050 Text mining algorithms.mp4
├── 051 Text mining in R.mp4
├── 052 Text mining in Python.mp4
├── 053 Text mining in RapidMiner.mp4
├── 054 Next steps.mp4
└── Ex_Files_DSF_DataMining.zip