Anyone working on AI will definitely know that how difficult it is to make computers do the common things that human beings are good at. Machine learning is one of the fields in computer science that I am always interested in.
While experimenting with various machine learning algorithms, Weka is my choice of tool.
As quoted from the website, the overall goal of Weka project is to build a state-of-the-art facility for developing machine learning (ML) techniques and to apply them to real-world data mining problems. Several standard ML techniques are incorporated into a software “workbench” called WEKA, for Waikato Environment for Knowledge Analysis. With it, a specialist in a particular field is able to use ML to derive useful knowledge from databases that are far too large to be analysed by hand. WEKA’s users are ML researchers and industrial scientists, but it is also widely used for teaching.
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.
In coming articles I will describe how I used Weka to solve some common practical problems.