Hyderabad 9652220840 seshi.jonnala@zetasys.in

zetasys machine learning capabilities

http://www.zetasys.in is rewriting weka swing ui as j2ee web application to showcase its machine learning capabilities. As a first step, experimenter is taken.It is developed using jsf, esp., primefaces library. There are various ml libraries floating around in many a language. A typical solution can not be COTS product. It should be flexible like j2ee web app.

A view of zetasys machine learning front end, http://zml2-zml2.7e14.starter-us-west-2.openshiftapps.com/zml/experimentor.xhtml:

A search in google about weka at github returned the following:
MEKA
Multi-label classifiers and evaluation procedures using the Weka machine learning framework. http://meka.sourceforge.net/
MOA
MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. http://moa.cms.waikato.ac.nz/
WEKADEEPLEARNING4J
Weka package for the Deeplearning4j java library https://deeplearning.cms.waikato.ac.nz/
OPENML-WEKA
Package for uploading Weka experiments to OpenML.
https://github.com/openml/openml-weka
AUTOWEKA
provides automatic selection of models and hyperparameters for WEKA.https://www.cs.ubc.ca/labs/beta/Projects/autoweka/

TITANIC EXAMPLE
Example code for solving the Titanic prediction problem found on Kaggle.
https://github.com/birchsport/titanic
IMAGEJ
https://imagej.net/Trainable_Weka_Segmentation

WEKA PACKAGES
Plenty of choice. visit: http://weka.sourceforge.net/packageMetaData/

 

We look forward to your projects on machine learning at the earliest!

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