My two bits. A bit pedagogical, so apologies for covering things you might already know.<p>Python effectively started in the early 1990s. Perl was the hot new language for "scripting", but Perl 4 was lousy as an embedded language and didn't support extensions. This was fixed in Perl 5, which also made it possible to do complex data structures. I think early on the extension mechanism was also Unix-centric, and require a Makefile?<p>Tcl was amazingly easy to embed and extend. It was designed with that in mind. It wasn't so good at complex data structures. OO programming was an add-on, with several different available extensions. I also recall some problems with garbage collection of extension objects ... it's been a while.<p>Python was possible to embed, and easy to extend. It had support for more complex data types. It was a better fit than Perl 4 or Tcl for 2D and higher arrays. As such, it was picked up by the more numerically oriented developers, who used Python to, using the popular phrase from then, 'steer' computationally intensive jobs.<p>Python-the-language also changed to make it easier to support multi-dimensional array indexing, specifically for numerical computing.<p>(Perl at the time was very popular in bioinformatics, which at that time involved a lot of RE-style pattern matching, a lot of format munging, and a lot of making different pieces work together - things that Perl excels at. But not numerical computing.)<p>As a result, Python had several iterations of Numeric/numarray/NumPy while Perl, Ruby, and Tcl - languages which were used more by non-numeric subfields - did not.<p>Then came the web. First Zope, and later Django, helped make Python a popular language for web development. The easy extension mechanism made it easy to add all sorts of C extension modules.<p>And Fortran extension. And R extensions through adapters.<p>A lot of the early ML code was written in C, C++, Fortran, or R. The natural solution is to put the data into a NumPy array, pass it via some bindings to the other language, and continue to work with it in Python.<p>I don't think Perl and Ruby had the same level of numeric support + extensions, making it more difficult. Python got critical mass, and continued to grow.<p>People have been doing machine learning with Python since the 1990s. I can't think of who the "couple influencers" might be.