weka

Proficient in data science, data analysis (using R + Weka + Mathematica + Go + Ruby).

You can find the many industrial grade algorithms in Python, Spark, Julia, Weka etc.

Technical merits aside, Weka is GPL-licensed (commercial licenses available), and this library Apache 2.0.

I ended up using Weka for parts of the class, and python using scikit-learn for the other parts.

Skills needed would be mostly SQL, Java or.net, with a variety of technologies like lucene, weka, etc.

Mahout and especially Weka are popular in the field, both written in java.

In addition to its own algorithms, it has a plugin that wraps Weka so you get all those if you want them too.

Mahout has bad implementations and Weka is showing it's age.

I get that this site has weka and opencv, etc on their servers so i don't have to run it on my computer...but how does that change the game exactly?

Pertaining to Algorithmia - many pages list little to no information (i'm ignoring the weka/opencv/already open source implementations just put into the site for shits sake) as to what is being done to compute the output.

I'm not very experienced in Machine learning, just dabbled around a bit, so maybe someone could explain me this: Looking at the graph number of trees vs accuracy, I would have expected that the line would asymptotically reach a maximum accuracy given more and more trees; however for weka it looks quite wavy and for mahout it even looks as if there's an optimum and more trees are worse.

I would learn thoroughly lib/frameworks like "R" (and ggplot2, very cool), weka and the python suite numpy / scipy, matplotlib ----------------------------- look at all the books out now.

I'm not mega familliar with NLP as a field, but abandoning Java means you abandon Weka, right?

Weka (which is rarely used these days anyways) is for smaller problems and experimentation.

Weka is definitely more old-school, but it has a LOT of algorithms available.