Advanced Data Mining with Weka

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 clusterers

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

This course is aimed at anyone who deals in data

You should have completed Data Mining with Weka and More Data Mining with Weka or be an experienced Weka user

Although the course includes some scripting with Python, you need no prior knowledge of the language

You will have to install and configure some software components; we provide full instructions