As machine learning continues to grow in popularity, more and more developers are looking to incorporate this powerful technology into their applications.
However, in order to build effective machine learning systems, you need to have a strong foundation in programming.
In this blog post, we’ll take a look at the top 15 programming languages for machine learning, based on popularity, ease of use, and suitability for the task at hand.
Top 15 Programming Languages in 2023
Python is the most popular programming language for machine learning, and for good reason. Its simple syntax and extensive library of machine learning frameworks make it easy for developers to get started with machine learning.
According to a survey by Kaggle, it was revealed that
“Over 75% of data scientists use Python for their work.”
Python’s popularity is reflected in the large number of machine learning libraries available for the language, including TensorFlow, Keras, and PyTorch.
R is another popular programming language for machine learning, particularly in the fields of statistics and data analysis.
Moreover, R has a strong community of users and developers, which has led to the creation of many powerful machine learning libraries, including the popular caret and mlr packages.
Java is a widely used programming language in the enterprise world, and it’s also a popular choice for building machine learning applications.
While Java may not be as user-friendly as Python or R, it has a strong ecosystem of machine learning libraries, including Weka and Deeplearning4j.
Sebastian Raschka stated that,
“Java provides an excellent platform for machine learning, with many powerful libraries and frameworks available to developers.”
Julia is a relatively new programming language that was specifically designed for scientific computing and machine learning.
Furthermore, it is known for its high performance and ease of use, making it a popular choice for data scientists and machine learning researchers.
C++ is a powerful programming language that is widely used in the development of high-performance machine learning applications.
While C++ may not be as user-friendly as some of the other languages on this list, it is known for its speed and efficiency, making it a popular choice for applications that require real-time processing.
Scala is a programming language that is designed to be both functional and object-oriented, making it a popular choice for building large-scale machine learning applications.
Additionally, it is known for its scalability and high-performance capabilities, and it has a number of machine learning libraries available, including Spark MLlib and Breeze.
According to Dean Wampler
“Scala is an excellent language for machine learning due to its functional programming capabilities, which allow for concise and elegant code.”
MATLAB is a programming language that is widely used in the fields of engineering and science, and it’s also a popular choice for building machine learning applications. It has a number of machine learning toolboxes available, including the popular Statistics and Machine Learning Toolbox.
Go is a relatively new programming language that was developed by Google, and it’s quickly gaining popularity among developers.
While Go may not have as many machine learning libraries available as some of the other languages on this list, it’s known for its speed and efficiency, making it a good choice for building high-performance machine learning applications.
Daniel Whitenack said that,
“Go is a fast and efficient language that is ideal for building machine learning systems that require high performance.”
Swift is a programming language that was developed by Apple, and it’s primarily used for building iOS and macOS applications. However, Swift is also gaining popularity in the machine learning community, thanks in part to the release of Apple’s Core ML framework, which allows developers to build machine learning models directly in Swift.
Lua is a lightweight and efficient scripting language that is often used for game development and other performance-critical applications. It is also popular in the machine learning community for its ease of use and flexibility.
As per Andreas Mueller,
“Lua’s flexible and extensible design make it a natural choice for building complex machine learning systems that require custom data structures and algorithms.”
Prolog is a logic programming language that is often used for developing expert systems and artificial intelligence applications. Its syntax is based on first-order logic, which makes it well-suited for solving complex problems involving large amounts of data.
Rust is a systems programming language developed by Mozilla that is designed for building fast and secure software. It is popular for web apps and network servers, and gaining popularity in machine learning due to speed and efficiency.
Groovy is a dynamic programming language that is built on top of Java. It has a syntax similar to Java, but with added features such as closures and dynamic typing.
Groovy is often used in machine learning applications because of its simplicity and ease of use.
It has active community and ML libraries for data analysis, neural network modeling, and NLP.
Choosing the right programming language for machine learning depends on experience, task, and available libraries and frameworks.
Python is currently the most popular language for machine learning, but that doesn’t mean it’s the only choice. Depending on your needs and preferences, any of the languages on this list could be a great option.
When choosing a language for machine learning, it’s also important to consider the community and resources available for that language. A strong community can provide support and resources that can be invaluable when you’re just getting started with machine learning.
Great online resources available to learn programming languages for machine learning. One great place to start is HazeHunt, a website that provides news and information about technology, AI, and machine learning.
HazeHunt offers a wealth of resources for developers and data scientists, including tutorials, articles, and news updates.