Extend your repertoire of data mining scenarios and techniques
This course will bring you to the wizard level of skill in data mining, following on from Data Mining with Weka and More Data Mining with Weka, by showing how to use popular packages that extend Weka’s functionality. You’ll learn about forecasting time series and mining data streams. You’ll connect up the popular R statistical package and learn how to use its extensive visualization and preprocessing functions from Weka. You’ll script Weka in Python – all from within the friendly Weka interface. And you’ll learn how to distribute data mining jobs over several computers using Apache SPARK.
What topics will you cover?
- Time series analysis
- Data stream mining
- Incremental classifiers
- Evolving data streams
- Support vector machines
- Accessing data mining in R
- Distributed data mining
- Map-reduce framework
- Scripting data mining in Python and Groovy
- Applications * Soil analysis * Sentiment analysis * Bioinformatics * MRI neuroimaging * Image classification
What will you achieve?
By the end of the course, you'll be able to...
- Discuss the use of lagged variables in time series forecasting
- Explore the use of overlay data in time series forecasting
- Identify several different applications of data mining with Weka
- Compare incremental and non-incremental implementations of classifiers
- Evaluate the performance of classifiers under conditions of concept drift
- Classify tweets using various techniques
- Calculate optimal parameter values for non-linear support vector machines
- Demonstrate the use of R classifiers in Weka
- Develop R commands and R scripts from Weka
- Explain how distributed Weka runs Weka on a cluster of machines
- Experiment with distributed implementations of Weka classifiers and clusters
- Explain how “map” and “reduce” tasks are used to distribute Weka
- Design Python and Groovy scripts for Weka operations
- Apply Python libraries to produce sophisticated visualizations of Weka output
- Describe how Weka can be invoked from within a Python environment
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Last updated February 17, 2018