Having recently started work on the Udacity MiniProject #1 from the Intro to Machine Learning course, what again started as a simple verification that all the python code and libraries worked ended being an interesting dive into handling text data and validating results.  The MiniProject uses a subset of the Enron email corpus to determine the email author id accuracy.  (The Enron corpu

Having previously completed the Naïve Bayes and Gaussian Naïve Bayes implementations in Java, it was just a matter of figuring out how to match the Udacity Intro to Machine Learning python logic and chart the data.  A good learning experience and the Java results are similar.  The Java code is here.  

Confronted with implementing a Gaussian Naïve Bayes I first needed to understand (and implement) the classification and prediction of a Naïve Bayes.  I found that most of the machine learning frameworks, while implementing some form of the algorithm, never explained why they made some decisions in the coding, nor obvious ways of testing that classification / prediction is consistent with the formula.  I ended up writing code to implement the Male/Female Drew examples as explained by professor Eamonn Keogh at UC Riverside,

Having dived into my first Udacity machine learning introductory course in May 2016, I was suddenly confronted with a complete Python machine learning ecosystem.  It is difficult enough overcoming Python's propensity for not defining anything beforehand.  It means digging through a ton of documentation, library code, or testing to figure out the structure of returned variables.  But on top of that are the numpy, sklearn, mathplotlib and pylab python libraries that the Udacity courses are leveraging, which also hav

There's the old saying that when all you have is a hammer, everything looks like a nail.  That's pretty much been my philosophy in terms of computing languages -- when it comes to the programming problem, Java is my hammer.  With a side bit of UNIX Bash scripting for quick data problems and gluing together solutions.  Outside of academia languages, which I do not even pretend to follow,  or statistics languages, or frameworks, is it really necessary to know everything?  I'll peruse the articles in IEEE Computer or my ACME magazines, but that's about it.

When I started to dig into what Machine Learning education is available I was pleasantly surprised to find that there is a nice selection to choose from in the MOOCs (Massive Open Online Courses) area.   There might be more but I've looked at the following from the big three.  From my novice point-of-view none of them look easy.  And none are Java based using python.