Machine learning is a subcategory of artificial intelligence that aims to give computers the capability to make decisions based on previous data and experiences. In this discipline, developers aim to minimize human-computer interaction through pre-defined algorithms and datasets. Machine learning programming is a broad field that cannot be satisfied by a single programming language. Your choice of a programming language for a machine learning project should be informed by the type of plan you will be working on and learning background, among other factors.

The five most widely used machine learning languages

1. Python

Python is the undoubted king of AI, IoT, and machine learning in particular. Data released by GitHub indicates that it is the most widely used language for machine learning projects. It also reveals that about 33% of machine learning developers prefer to use Python over any other programming language. 

Python is very easy to use and has a straightforward syntax that allows developers of all skill levels to implement algorithms effortlessly. The numerous python libraries for data science, artificial intelligence, and machine learning, have contributed significantly to its popularity. Some of the python libraries used in machine learning code include Pybrain, Keras, Scikit-learn, Teano, and Numpy. Python also works seamlessly with TensorFlow thus 

2. Java

Java programming language is popular in making enterprise-level systems. It is considered one of the most se­­cure programming languages and is therefore preferred for systems revolving around network security, cybersecurity, and fraud detection, among other things.

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Java has asserted itself as the best alternative to python programming language for machine learning. Its straightforward debugging process, excellent package services, usability with large projects, and a rich graphical application development platform attracts a large percentage of enterprise developers.

The release of Java 11 has enhanced the language’s support for machine learning by improving string processing, file handling, and pattern recognition. Java’s strict OOP structure has seen machine learning algorithms designed to work for large projects. You need to learn machine learning with Java if you are looking for a job in corporate environments.

3. C++

It is not a surprise that we have C++ among the top three machine language programming languages. The language remains a preferred candidate for the development of all-round programs. C++ experts will tell you that there is no limit in what you can achieve with the language’s easy to follow OOP syntax. For machine learning, C++ boasts support for TensorFlow- a machine learning platform by Google, Turi Create, and LightGBM, which are robust frameworks for creating efficient machine learning models.

Developers also prefer C++ so they can leverage the speed and efficiency of the language. C++ has reliably fast access to the underlying system and can be used to implement fast and precise ML algorithms. The low-level features of the language is an added advantage to machine learning programmers as they can utilize them to create advanced functionalities for ML programs.

4. R

Data analysts and statisticians cannot get a better language for machine learning. R programming language is used for processing, analyzing, and visualizing statistical data. R programming has a solid history with data and has found favor with data engineers working for big firms like Google and Facebook.

In machine learning, R comes handy for tasks like decision tree formation, classification, and regression analysis. It is also widely used for machine learning projects in bioinformatics, biostatistics, and bioengineering. Machine learning with R is accomplished through packages like Gmodels, Class, TM, and ROBDC.

Unlike Python, Java, and C++, which integrate well with continuous projects, R is best suited for small projects that include sums of data and artifacts.

5. JavaScript

JavaScript is the preferred language for a large percentage of web developers. Like Java, JavaScript can be used to create enterprise-level machine learning systems. With this language, developers can easily visualize machine learning data, experiments, and outcomes on the web without creating complex GUI applications.

Like Python, JavaScript can be used to implement machine learning algorithms in either the OOP, structural, or functional formats. Also, there are several high-level ML libraries and tools that work well with JavaScript. People with a background in web development will find it easy to implement ML algorithms that they would do in Java or C++.


As far as machine learning languages are concerned, the top five languages are only the tip of the iceberg. However, the languages we have mentioned have a combined market share of more than 75% in the ML and AI industry. This means that you will need to be aware of at least one of the languages if you will be looking for a job in machine learning development.

Although the project at hand determines the language choice, Python is hailed by many developers due to its easy-to-learn syntax and is therefore recommended for absolute beginners.