On the other hand, a late stage of machine learning work can involve implementing algorithms on a massive scale, with large datasets, where careful control of data type, bit resolution, parallel threading and such may become more important.

I'm not aware of any other language that comes close to its power and ease in such tasks. I really like Mathematica's symbolics for statistical analyses, Probability, and so on. One of the many great benefits of Mathematica is its seamless integration between different functionality, so it is easy to use statistical learning with graph-theoretic methods, and pre-processing of images, sound, financial data, etc., without the need to load libraries of special-purpose functions. In pattern recognition and machine learning, the early stages are ones of experimentation and exploration-trying different algorithms, feature pre-processing, and such, as well as integrating different functions and visualizing preliminary results. Here, as in most disciplines, the 'best' language depends upon what you seek to accomplish. As such, I feel I'm fairly well positioned to answer this question.

I have worked in pattern classification and machine learning for decades, taught the subject in a number of elite academic departments, am writing the third edition of Pattern classification by Duda, Hart and Stork as well as its companion computer manual in Mathematica, and am an expert Mathematica programmer, a solid Matlab programmer, but very weak in R and Python and have no experience with Julia.

Mathematica for machine learning