Weka Mini Course

A 14 step mini course by Jason Brownlee on using the machine learning system Weka.  Notable because it uses no programming code.  Nice for those procedurally rather than code oriented.  I can see some people liking this approach to understand the concept of machine learning.   Though you can make the case that ultimately the approach should be understood technically to better detect errors in process.   Still I like the alternative.  Notably too, I have yet to see Weka used in the enterprise versus Python or R.

Alternative Machine Learning Environments

I am starting to examine Machine Learning Platforms for an application.  This post is mostly for my own and a few collaborators use.  Not complete or just open source.  Do not claim to be expert in any of these methods, but have encountered them in a number of very differing machine learning contexts. In some cases used for building a system from the ground up, in other cases reviewing or extending existing modeling efforts.  R is not included here, it is more of a programming language than a learning platform.  All these are worth examining.  Open to alternative ideas.

IBM SPSS Modeler (Which I formerly used as Clementine):  http://www-01.ibm.com/software/analytics/spss/products/modeler/

Weka:  http://www.cs.waikato.ac.nz/ml/weka/

Also Orange Data Mining   http://orange.biolab.si/

And BigML Data Mining:  https://bigml.com/how_it_works

Running a Simple Classifier

Been following Machine Learning Mastery, by Jason Brownlee.   He posted an item about a free classifier in Weka that I had not seen before.   He includes a step by step approach that is worth a look  Getting more tools to the people is a good idea.  In some cases they can use the tools themselves,  in other cases t can give an enterprising decision maker an understanding of what is required to make such a classifier useful.   Some like delving into the quantitative details.  Thanks Jason.