Machine Learning Languages -

1. Python

Python is the leader, with 57% of data scientists and machine learning developers using it and 33% preferring it over other languages for developments. Not only python is a widely-used language, but it is the primary choice for most of its users due to the release of TensorFlow and a wide selection of other libraries.

Python is the best choice for beginners in the field. There are plenty of python libraries such as Teano, Keras and scikit-learn that are available for machine learning, deep learning, artificial intelligence, NLP, etc. For example: Numpy is its library that helps to solve many computations and Pybrain is for using machine learning in python.

Another reason for its popularity is that its syntaxes are very simple and can be easily learnt which makes algorithms easily implemented. It gives direct access to its users for predictive analytics. It is a preferred language for developers who are looking to frame better questions and expand their capabilities of existing machine learning systems.

2. Java

Java is the second most preferred language used by data scientists and machine learning developers, with 15% experts using it for network security / cyber attacks and fraud detection where python is least preferable.

Java is a very easy-to-use language that provides with simple debugging process, huge package services, work simplification in large projects, graphical representation of data, and better user interaction.

The newest version of java that is java 11, has improved features for machine learning such as:

  • New string methods- isBlank, lines, repeat, stripLeading, stripTailing, and strip
  • New file methods- writeString, readString, and isSameFile
  • Pattern recognizing methods- asMatchPredicate, etc.

Java is regarded as a secure language due to its use of bytecode and sandboxes. It is no surprise that the latest as well as older machine learning algorithms are written in Java. It is a functional programming language that will allow future machine learning systems with speed, accuracy, and precision. Prepare to use Java, if you’re going for a job in enterprise environment.

3. R

R is a graphic-based language used for statistical computing, analysis, and visualizations in machine learning. For those who want to explore statistical data via graph, it is the perfect platform. It is also used for a variety of purposes by data scientists at facebook, google, and many other big companies.

It is a very popular programming language among statisticians, and is also applied to machine learning tasks such as regression, classification, and decision tree formation.

R is highly preferable in bioengineering, bioinformatics, and biomedical statistics. The packages used for machine learning are: RODBC, Gmodels, Class, and TM. It is suited for one-off projects such as reports, research papers, that includes artefacts.

Python is often compared to R, you should know that these two are completely different languages used for different purposes. For more on these two you can read their differences between Python and R in detail.

4. JavaScript

JavaScript is the most widely used language to improve web development. Developers who are new to data science and machine learning prefer javascript for enterprise focused applications. As it helps to visualize results of machine learning algorithms on a web-based dashboard.

It is a high-level, dynamic, untyped, object-oriented, interpreted language that is standardized in ECMAScript language specification. It makes machine learning usable even by non-technical background students by offering access to ML algorithms and models in the browser.

With the implementation of NodeJS, javascript has become one of the top rated programming languages and as the community of javascript programmers is increasing, NPM repositories are getting bigger.

JavaScript even helps in game development, mobile applications. It is a scripting language that provides a very high scope for learners in the future.

5. Scala

Scala is one of the core languages that is supported by Apache Spark platform. It is a comprehensive data platform that provides functionalities for big data processing and machine learning analysis through its MLLIB library.

This language provides programmers to develop, design, code, and deploy machine learning algorithms by making the best use of capabilities provided by Spark and other big data technologies. There are many well-developed libraries of Scala programming language suitable for linear algebra, random number generation, and scientific computing.

Saddle, a data library supported by Scala, provides a strong foundation for data manipulation through array-backed support, robustness to missing values, automatic data alignment, and 2D data structures.

Start your career in machine learning, by taking a quick quiz to test your Scala skills.


Python is the most popular, general purpose programming language suitable for a variety of tasks in machine learning. R is used for data analysis and statistical computations. The best language for machine learning depends on the area on which it is going to be applied.

Tell us in the comments section below, what programming languages do you use for machine learning and why do you use it!

Source :

Author : Himani Bansal