[ThePhysicist]

Pythons' USP was and still is its ability to provide a simple & intuitive "glue layer" over lower-level libraries. Most of the performance-critical functionality that Python relies on for ML is written in C/C++/Fortran and Python mostly provides the UI layer (this is an oversimplification of course).

Wrapper generators and compiler tools like Cython and before that SWIG made it very easy to glue existing functionality to Python, so together with Pythons' great usability and user-friendly language it created a killer combination for productive data science & ML.

That said other languages could've pulled this off as well, Ruby for example. Python had more early traction in the scientific and high-performance computing communities though whereas Ruby was more popular in web development (due to Rails), which ultimately gave Python the edge and attracted more and more toolmakers to its ecosystem, which in turn spurred further growth. Great "IDEs" like the iPython/Juypter notebook were also a key factor in Pythons' success, as they provided a super user-friendly UI for data scientists.