Many professionals entering the field of Machine Learning (ML), Artificial Intelligence (AI), Natural Language Processing (NLP) and Data Science in 2019 must choose a programming language to implement their projects. Tech community forums are abuzz with questions on how to pick the right language and there are equally diverse answers which can be confusing to many. Here’s a primer on some of the more popularly used programming languages that will be useful for developers and programmers for ML algorithms in 2019.


Python is one of the most popular languages used in AI and machine learning due to its simplicity. It is widely adopted, easy to understand, and offers rapid prototyping compared to other programming languages like C++ or Java.  Python supports the different styles of programming – object, functional and procedure oriented. Python also comes with many libraries that are useful for ML developers. For example, Pandas, Scikit-learn and Pybrain are popular Python toolkits used in machine learning. OpenCV is used for image recognition, TensorFlow and Pytorch are useful in deep learning and Scipy is used in scientific computing. These are easy to implement and free to use under a GNU license.


Java has been in existence earlier than Python and works well in machine learning. The advantages of using Java are many – a large base of programmers, ease of use, maintenance and debugging and readability. The most popular ML toolkit for Java is Weka which contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. Another open source library is DeepLearning4j which is specially designed for Java to provide support for deep learning algorithms. This one combines deep neural networks and deep learning reinforcement for use in diverse applications. This library is useful in ML algorithms that require identifying sentiment and patterns in speech, text, and sound. It is also useful in finding out outliers or any deviations in time-sensitive or historical data.


Unlike Java and Python that are programming languages, R is a full-fledged software environment used for statistical computing and visualization. R has been in use for a long time in academia and its application is seen in areas like bioengineering and bioinformatics. R is free and open source which means users can access it easily, learn from the source code and modify it as per requirements. It gives users access to a substantial number of leading-edge packages, many of which are created by academic experts in statistical fields. These packages or libraries enable the easy implementation of ML algorithms such as Nnet and CARET.

C /C++

C/ C++ are used when users need rapid execution of a project. Best use cases of C/C++ are in the area of AI in games and robot locomotion, given that these require high levels of control, performance, and efficiency.  C/C++ has sophisticated libraries like Mlpack. It is a fast, flexible machine learning library that aims to provide fast, extensible implementations of cutting-edge ML algorithms.


Julia is a newer, high-level, general-purpose, free programming language that is being rapidly adopted by the tech and finance communities for its ability to rapidly execute ML algorithms. It has already been applied in high profile business use cases – by investment banks which have used it for time series analytics, to insurance companies that have used it for risk calculation. The advantage of the language is that it comes equipped with Flux, a state-of-the-art framework for ML and AI. Flux provides a flexible and intuitive, layer-stacking-based interface for simple models which can be modified to create more advanced models. Julia is also adaptable to existing workflows, as it has support for other ML frameworks like TensorFlow and MXNet.

Choosing the right programming language

Programmers must choose the language based on what the needs of the project are, and whether there are requisite libraries available for that language. For example, Java and Python are usually popular in NLP as most modern NLP tools and libraries are written in these two languages. NLP libraries in Python include NLTK, Polyglot, and Scikit-learn. NLP libraries in Java include Stanford Core-NLP, Apache Open-NLP, Apache UIMA.  For more on NLP, read our blog.

Java is usually prioritized in network security and fraud detection algorithms in financial institutions. C/C++, on the other hand, is often by companies who would prefer to enhance their existing legacy apps/projects with ML, rather than build new ones.

In conclusion, while there is no single ‘best’ language for an ML project, users are advised to choose one that will work best for that project. As ML becomes more complex, the creators of ML systems have big challenges, but with the right mix of programming languages, these challenges can be overcome.