Waikato Environment for Knowledge Analysis
Weka is an acronym that stands for Waikato Environment for Knowledge Analysis. It is also the name of a New Zealand bird the Weka.
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.
The advantage of a package like Weka is that a whole range of data preparation, feature selection and data mining algorithms are integrated. This means that only one data format is needed, and trying out and comparing different approaches becomes really easy.
WEKA’s users are ML researchers and industrial scientists, it is also widely used for teaching.
In 30 minutes I will show how to use Weka. What is feature engineering and how to generate feature set. How to load feature set in Weka. Which formats of feature sets are supported by Weka. What classifiers are available at weka and how to use them. How to install third-party classifiers in Weka. How to visualize your pre-processed data.
Bonus: How to solve the memory-related issue in the time of running Weka.
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