Top 5 Programming Languages for Artificial Intelligence You Should Learn

The popularity of artificial intelligence is increasing rapidly. Which languages are the best for programming AI-based solutions? Read in the article!

With the rising popularity of AI, the demand for talented and skilled Artificial Intelligence Engineers also increases. Artificial intelligence is the future, so many software developers decide to improve their skills in this field. And what programming language will be the best for creating AI-based solutions? Check out our subjective list of the top 5 programming languages for artificial intelligence!

1. Python

Over the last few years, Python is still the best and most popular language for programming AI and ML-based solutions. It is a leader of most of the rankings in this category. Its most significant competitive advantage is its simplicity of use and the vast number of available machine learning libraries and frameworks. 

Python is also one of the easiest programming languages, so it is a popular choice not only for programmers but also for mathematicians, physicists, or analysts related to artificial intelligence.

Popularity overall

According to Stack Overflow, Python is the fourth most popular programming language overall and also the fastest-growing major programming language. 

Moreover, it is the leader among the most popular machine learning languages on Github.

Pros of using Python for AI:

  • easy to learn and read
  • a vast number of machine learning libraries and useful tools
  • popularity and large community
  • many useful machine-learning repositories
  • easy to integrate with other programming languages

Cons of using Python for AI:

  • speed limitations
  • not suitable for mobile and game development
  • design limitations

Useful AI & ML libraries

  • TensorFlow - fast, flexible library for working with datasets and machine learning
  • PyTorch - a machine learning framework that speeds up the path from research prototyping to production deployment
  • Scikit-Learn - a library focussed on data modeling. It provides simple integration with other ML programming libraries
  • Keras - an open-source library for machine learning and deep learning, which is used for complex mathematical calculations and fast datasets processing

2. Java

Java is one of the most popular and best-paid programming languages in the world. It is a general-purpose programming language, which means it is very flexible. It is much more challenging to learn than Python and requires much more time for learning and coding in this language.

Java is a statically-typed language, so it is relatively easy to debug. Software developers love it for its high user-friendliness and ability to work on most platforms. Moreover, Java is a very safe and scalable language, and it is an excellent solution for large scale projects.

It is worth noticing that the newest version of Java has improved a few useful features for machine learning, such as new string methods, new file methods, and pattern recognition methods.


According to the Stack Overflow, Java is the 5th most popular technology and the 3rd most popular backend programming language in the world. 

Currently is in the 4th place of the top machine learning language on the Github ranking. 

Although its popularity has declined in recent years, it is still a stable language with a strong market position.

Pros of using Java for AI:

  • easy to implement on various platforms
  • easy to debug
  • popularity and large community
  • a lot of open-source libraries
  • good for mobile applications

Cons of using Java for AI:

  • needs a JVM to function
  • a high entry threshold

Useful AI & ML libraries:

  • Weka - an easy-to-use library for data analysis, data mining, and predictive modeling
  • Massive Online Analysis (MOA) - library for machine learning on data streams in real-time, especially useful for large datasets and the Internet of Things (IoT)
  • Java-ML - a collection of machine learning algorithms for feature selection, data preprocessing, classification, and clustering

3. JavaScript

JavaScript is built-in in all web browsers, so it does not need downloading any compilers or extra software. This language is relatively easy to learn, so it will be a good choice for beginners.

JavaScript is a front-end language primarily used to build:

  • dynamic websites,
  • standard web applications,
  • progressive web applications.

But why is JavaScript also the right choice for Artificial Intelligence? First of all, it has a few high-level tools and libraries for Machine Learning. A good example is TensorFlow.js, which runs directly within the browser and opens up a lot of new possibilities for developers. Moreover, it allows visualizing the results of ML algorithms on a web-based dashboard. 


According to both Stack Overflow and Github rankings, JavaScript is the most popular technology in the world. It is a reliable and unbeatable leader among all technologies. 

When it comes to AI, its popularity is slightly less (3rd place among the most popular machine learning languages hosted on Github), but it certainly deserves the attention of Artificial Intelligence Engineers. 

Pros of using JavaScript for AI:

  • works well with other applications
  • good speed
  • a lot of useful fast-growing ML libraries
  • popularity and community support

Cons of using JavaScript for AI:

  • lack of debugging facility
  • client-side security
  • browser support (JS is interpreted differently in different browsers)

Useful AI & ML libraries

  • TensorFlow.js - a popular ML library for training and using machine learning models directly in the browser.
  • Brain.js - an open-source JS library for running and processing neural networks.
  • ml.js - a group of repositories and tools for ML, including regression algorithms, artificial neural networks, supporting libraries for statistics, and many more. 

4. R-programming

R is a dynamically typed language, considered one of the best programming languages used for statistics, predictive analysis, and other activities related to data science. R is easy to understand, especially for people who programmed in another language. It does not require complex knowledge because it has a lot of ready to use packages, libraries, and materials that can help you in almost every step of the software development process.


According to the Stack Overflow, only 5.8% of software developers know R. It ranked at 17th place on the list of most popular technologies overall. 

Despite its low popularity, R often appears on the lists of the best programming languages for artificial intelligence. For instance, it is on the 8th place among the top machine learning languages on Github.

Pros of using R for AI:

  • good for statistics and analysis
  • good for crunching huge numbers
  • a lot of useful libraries, frameworks, and AI programming packages
  • allows working on various paradigms of programming 

Cons of using R for AI:

  • speed limitations
  • not beginner-friendly (recommended as a second programming language)

Useful AI & ML libraries

  • Dplyr – a powerful library with a simple syntax, which is used in the process of data manipulation
  • Ggplot2 – an old, extensive library for visualization and graphical representation of data

5. Go

Go is not as popular as Python, Java, or JavaScript, but it is one of the fastest-growing languages for machine learning. Its main advantage is speed - it is much faster than Python!

Despite its youth (it was launched in 2009), it has a lot of followers. Developers recommend it to work on machine learning infrastructure and large-scale projects.


Go is another niche language, but, as I mentioned before, it is one of the fastest-growing technologies! Comparing 2018 and 2019, the popularity of Go increased by 147%. It is a relatively young language, so it could be really useful in the future!

Pros of using Go for AI

  • good speed
  • embedded testing environment
  • a smart standard library
  • good for infrastructure projects

Cons of using Go for AI:

  • relatively few libraries
  • youth and low popularity

Useful AI & ML libraries

  • GoLearn –  a new library for machine learning, including a few useful methods and algorithms such as neural networks or and logistic regressions
  • GoML – a library used for generalized linear models, logistic regression, perceptron, text classification, and many more


Today we can find plenty of programming languages that can be used for various AI-based solutions, but we need to know that any single programming language is a one-stop-solution for AI. Today's IT market requires a specific approach for every project, so before you decide to use one of them, you should consider which one will best meet your expectations.

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