General number crunching/data analysis packages. (Specialist software is dealt with elsewhere. See machine vision, machine listening, gesture recognition, scientific workflow etc.)
R is a galaxy of statistical packages.
Sundry deep learning packages are on their own page.
Scala’s ML ecosystem is growing.
So is julia’s.
dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib’s open source licensing allows you to use it in any application, free of charge.
Includes a lot of kitchen-sink features such as a GUI toolkit and python binding.
SHARK is a fast, modular, feature-rich open-source C++ machine learning library. It provides methods for linear and nonlinear optimization, kernel-based learning algorithms, neural networks, and various other machine learning techniques (see the feature list below). It serves as a powerful toolbox for real world applications as well as research. Shark depends on Boost and CMake.
ELL is Microsoft’s open source thing targetting tiny processors and embedded devices
Vowpal Rabbit despite its abstruse project description, seems to be a good library for out-of-core linear learning i.e. regression or classification a stupendously large data set, but a model that is not stupendously large. Approaches include various online (in the sense of incremental, streaming) optimisations. L1/L2 regularisation. Linear or logistic models. (i.e. linear models). Squared, hinge, logistic, or quantiles losses. (Has a python binding btw, doesn’t everything?)
bash data science command line.