Overview
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Get started in data mining. Discover data mining techniques such as data reduction, clustering association analysis, and more, with data mining tools like R and Python.
Syllabus
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Introduction
- Welcome
- Who should watch this course
- Exercise files
- Data mining prerequisites
- Algorithm prerequisites
- Software prerequisites
- Goals of data reduction
- Data for data reduction
- Data reduction in R
- Data reduction in Python
- Data reduction in Orange
- Data reduction in RapidMiner
- Clustering goals
- Clustering data
- Clustering in R
- Clustering in Python
- Clustering in BigML
- Clustering in Orange
- Classification goals
- Classification data
- Classification in R
- Classification in Python
- Classification in RapidMiner
- Classification in KNIME
- Anomaly detection goals
- Anomaly detection data
- Anomaly detection in R
- Anomaly detection in Python
- Anomaly detection in BigML
- Anomaly detection in RapidMiner
- Association analysis goals
- Association analysis data
- Association analysis in R
- Association analysis in Python
- Association analysis in Orange
- Association analysis in RapidMiner
- Regression analysis goals
- Regression analysis data
- Regression analysis in R
- Regression analysis in Python
- Regression analysis in KNIME
- Regression analysis in RapidMiner
- Sequence mining goals
- Sequence mining algorithms
- Sequence mining in R
- Sequence mining in Python
- Sequence mining in BigML: Part 1
- Sequence mining in BigML: Part 2
- Text mining goals
- Text mining algorithms
- Text mining in R
- Text mining in Python
- Text mining in RapidMiner
- Next steps
