![]() In areas that are less enterprise-focused, such as natural language processing (NLP) and sentiment analysis, developers opt for Python which offers an easier and faster way to build highly performing algorithms, due to the extensive collection of specialised libraries that come with it.Īrtificial Intelligence (AI) in games (29%) and robot locomotion (27%) are the two areas where C/C++ is favoured the most, given the level of control, high performance and efficiency required. Network security and fraud detection algorithms are built or consumed mostly in large organisations - and especially in financial institutions - where Java is a favourite of most internal development teams. In contrast, Java is prioritised more by those working on network security / cyber attacks and fraud detection, the two areas where Python is the least prioritised. Machine learning scientists working on sentiment analysis prioritise Python (44%) and R (11%) more and JavaScript (2%) and Java (15%) less than developers working on other areas. Here we present the top and bottom three areas per language: the ones where developers prioritise each language the most and the least. In our survey we asked developers about 17 different application areas while also providing our respondents with the opportunity to tell us that they’re still exploring options, not actively working on any area. ![]() Our data reveals that the most decisive factor when selecting a language for machine learning is the type of project you’ll be working on - your application area. Python is prioritised in applications where Java is not. We therefore focused our attention on the top-5 languages. ![]() We asked our respondents about other languages used in machine learning, including the usual suspects of Julia, Scala, Ruby, Octave, MATLAB and SAS, but they all fall below the 5% mark of prioritisation and below 26% of usage. Java follows C/C++ very closely, while JavaScript comes fifth in usage, although with a slightly better prioritisation performance than R (7%). C/C++ is a distant second to Python, both in usage (44%) and prioritisation (19%). Not only is Python the most widely used language, it is also the primary choice for the majority of its users. The same ratio for Python is at 58%, the highest by far among the five languages, a clear indication that the usage trends of Python are the exact opposite to those of R. This means that in most cases R is a complementary language, not a first choice. R is in fact the language with the lowest prioritisation-to-usage ratio among the five, with only 17% of developers who use it prioritising it. Python is often compared to R, but they are nowhere near comparable in terms of popularity: R comes fourth in overall usage (31%) and fifth in prioritisation (5%). Little wonder, given all the evolution in the deep learning Python frameworks over the past 2 years, including the release of TensorFlow and a wide selection of other libraries. Python leads the pack, with 57% of data scientists and machine learning developers using it and 33% prioritising it for development. Which machine learning language is the most popular overall?įirst, let’s look at the overall popularity of machine learning languages. It depends on what you’re trying to build, what your background is and why you got involved in machine learning in the first place. We compared the top-5 languages and the results prove that there is no simple answer to the “which language?” question. Then, being data scientists ourselves, we couldn’t help but run a few models to see which are the most important factors that are correlated to language selection. We turned instead to our hard data from 2,000+ data scientists and machine learning developers who responded to our latest survey about which languages they use and what projects they’re working on - along with many other interesting things about their machine learning activities and training. Τhere’s so much more activity in machine learning than job offers in the West can describe, however, and peer opinions are of course very valuable but often conflicting and as such may confuse the novices. There’s an abundance of articles attempting to answer these questions, either based on personal experience or on job offer data. Q&A sites and data science forums are buzzing with the same questions over and over again: I’m new in data science, what language should I learn? What’s the best language for machine learning? What is the best programming language for Machine Learning?
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