Why Python Is the Top Choice for Machine Learning


There are hundreds of programming languages in existence today, and each has its utilities. When it comes to machine learning, however, there can be only one true champion: Python.

Released all the way back in 1991, the multi-paradigm programming language is renowned for its simplicity and multitude of uses. Since then, it’s steadily grown in popularity, and – according to HackerRank’s recent software Developer Skills Report – was the fourth most commonly-known language in 2018. HackerRank’s data suggests that Python may climb further up the ranks, too, as it was the third most popular language that software developers wanted to learn in 2019 (trumped only by Go and Kotlin, both of which rank much lower as currently-used languages).

The reasons that programming languages fluctuate in use and popularity are obviously various and nuanced, but, with Python, it’s very clear that the progress made in machine learning has had an impact.

But what sets it apart from its competitors?

Compared to C++, the fifth most-used language, Python may be slower – but speed isn’t everything. Especially in AI contexts, where processes can quickly become complicated, simplicity and functionality are far more important. This is where Python syntax demonstrates a clear advantage over C++; not only is it more straightforward to use, it also runs on all platforms without encountering too many problems.

While it’s excellent for game development, then, C++ gets left in Python’s dust in the realm of machine learning.

Python’s other major competitor is, of course, Java – the most popular programming language worldwide. Python may trail behind C++ in terms of speed, but it outdoes Java very easily in this field. Even if that wasn’t the case, Java is a compiled language, meaning it can only run on the platform it was compiled on. Python, meanwhile, is interpreted, making it infinitely more versatile.

R is also frequently mentioned in AI-programming circles. A comparison of the two published by Norm Matloff, a professor of computer science at the University of California Davis, found that both languages were superior to other rivals in many ways – but Python still has the edge where it counts.

“The R-vs.-Python debate is largely a statistics-vs.-CS debate, and since most research in neural networks has come from CS, available software for NNs is mostly in Python. RStudio has done some excellent work in developing a Keras implementation, but so far R is limited in this realm,” he writes.

R did triumph in the categories of ‘learning curve’, ‘statistical correctness’ and ‘C/C++ interface’ – but Python stole the show when it came to ‘machine learning’, ‘elegance’ and ‘language unity’.

In general practice, too, Python has simply proven to be more popular. In a 2018 Cloud Academy report, the training platform analysed data engineer job descriptions in order to see which programming languages were most sought-after in data engineer positions. It found that almost 66% of job postings mentioned Python, while only 18% mentioned R.

“Python is known to be an intuitive language that’s used across multiple domains in computer science,” the report stated. “It’s easy to work with, and the data science community has put the work in to create the plumbing it needs to solve complex computational problems. It could also be that more companies are moving data projects and products into production. R is not a general purpose programming language like Python.”

Hackernoon’s endorsement of Python also makes some excellent points. “One of the main reasons why Python so quickly became a staple of machine learning was its extensive libraries,” they explain. “You want a complex computational operation done on a massive amount of data? Python has the library for it.”

Some of the most useful libraries in question are PyBrain (a modular learning library that provides handy test environments), PyML (“a bilateral framework that focuses on support vector machine (SVM) and kernel methods”), and Scikit-learn (a specifically-designed, open-source machine learning library).

When it comes to choosing the best language for machine learning, though, the proof is in the pudding. Python is already being used by large organisations such as Google, Amazon, and Facebook, and is continuing to grow in popularity as time goes on. Things may change in the future but – for now – very few other languages compare.

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