javaSerdar Yegulalp of InfoWorld recently wrote, “After spending decades in the shadows as a specialty discipline, machine learning is suddenly front and center as a business tool. The hard part, though, is making it useful, especially to the developers and budding data scientists who are being tasked with the job. To that end, we rounded up some of the most common and useful open source machine learning tools we’ve spotted in the wild.”

He begins, “For Python: Data scientists have jumped on Python as a more open-ended alternative to analytical languages like R, and many employers looking to add big-data expertise to their rosters are listing Python as a desired skill. As a result, plenty of machine learning libraries have shown up in Python’s ever-expanding software roster. For Go: Google’s system language designed for parallelism seems like an ideal environment for writing machine learning libraries. A slew of smaller, more specific libraries pepper the landscape, but a few general ones stand out. The most notable, GoLearn, is described by its creators as a “batteries included” machine-learning library, and it has tools for filtering, classification, and regression analysis. A much smaller and more basic library, mlgo, implements only a small number of algorithms at this time, but more are planned for the future.”

Read more here.

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