There is definitely an established machine learning ecosystem, or, perhaps more accurately, a small set of established machine learning ecosystems. For research it would seem that the undisputed champion of machine learning ecosystems is centered on Python and its many libraries which support the data preparation and subsequent machine learning process itself, whether it be via scikit-learn, one of the many deep learning libraries available, or home-spun and highly specialized tools for achieving the same goals. This says nothing of the great support tools that grow up around the edges of the ecosystem, some of which become polished and useful enough to carve out their own eventual niche.
As those in industry would be the first to let me know, Python is not the only option. There are Java-based tools (Deeplearning4j, Weka), those integrated with Apache Spark and/or Hadoop (MLlib, Mahout), C++ solutions (TensorFlow is written in C++, as are many others in the Python ecosystem), and even those for Clojure, F#, Rust, and a whole host of other languages, environments, and ecosystems.
Javascript
Javascript does not generally come up when talking about machine learning-friendly programming languages, however. Given its formidable market share and Atwood's Law, which states that any application that can be written in JavaScript, eventually will be written in JavaScript, it would seem that there may be something more going on here. Atwood's Law may not be the foregone conclusion it once was (at least, not in the view of many), but one could argue its validity vis-a-vis machine learning as easily as one could argue its validity vis-a-vis anything else.
So, why not Javascript? There are reasons for that. Many of them are not specific to Javascript, but address any non-Python (or non-established ecosystem) language. In other words, that's just how it is. Others touch on technical issues of speed, ease of reading and writing code, and complexity of the environment, among others.
But let's just be clear: foregoing any discussion of Turing Machines, theoretical computer science, or statistical processes, machine learning can be accomplished with Javascript. The options? Code something up yourself, or have a look at the following, a small sampling of both general machine learning and neural network libraries for Javascript. Some libraries actually also use Node.js, to be clear; check it out if you are unfamiliar.
The process for selecting the "top" libraries for Javascript was more art than science; given the somewhat comparative lack of options for the language, along with the challenge of finding well-used, supported, and maintained (the trifecta) projects, some subjectivity was necessary to come up with a list worth looking at.
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