Developers
June 17, 2020

What Are Some of the Top Machine Learning Languages and Why?

A career in machine learning development offers enough challenges and rewards for a lifetime.

Machine learning is an exciting field for developers. It offers a promising career at the forefront of computer research. To make the most of it, however, it’s important to invest in the right language for the job.

What Is Machine Learning?

Machine learning (ML) is a subset of artificial intelligence aimed at helping computer systems become more intelligent. ML algorithms continually improve with experience, eliminating the need to manually update them with new parameters.

Email filtering that learns with each true or false positive match is a good example of basic machine learning. According to estimates, by the end of 2023, the number of emails sent per day will reach 347 billion. With that many emails, it’s simply not realistic to rely on manual filters to stay ahead of junk mail. Instead, ML algorithms can improve the quality of filtering with a relatively small amount of time spent training them.

Social media recommendations are another example. As a user continues browsing stories and photos, the ML algorithms learn what a user likes and makes recommendations based on them.

The Best Languages to Use For ML Development

In order to make the most of a career in ML development, it’s important to choose the right language. There are a number of languages that are ideally suited for the field.

Python

Python is one of the premier ML development languages. Python started as a hobby of Guido van Rossum in 1989, before being officially released in 1991. The language has become one of the most popular languages in the world, consistently ranking in the top ten since 2003.

One of the things that makes Python so popular is its simple syntax. It is relatively easy to learn, compared to other programming languages. In addition, Python’s entire design philosophy emphasizes simple code and straightforward solutions.

What’s more, van Rossum created Python to be extensible. As a result, the language is very modular, designed around a small, central core, with its features and abilities augmented by a vast library of packages and tools.

Some of those libraries are specifically aimed at ML development. In addition, Python is popular among universities, where vast amounts of ML research is done and many breakthroughs occur.

All of this combines to make Python the language of choice for ML development.

Java

Java is another popular language for ML programming. Like Python, it consistently ranks as one of the most popular programming languages in the world.

Because Java was designed around the principle of Write Once, Run Anywhere (WORA), it’s an ideal language for the development of ML applications across platforms. In addition, like Python, it has a number of libraries to assist with ML development.

Because of its popularity, Java has a thriving developer community, which can be a big help for developers.

R

The R programming language is another popular choice for ML development. Originally released in 1993, it has gained in popularity with data scientists, data miners, statisticians and ML developers.

R runs on Linux, macOS and Windows, furthering its popularity.

C++

Like Java and Python, C++ consistently ranks as one of the most popular languages in use. Like the other languages, C++ has a number of libraries to aid in ML development.

C++ also runs on virtually every platform and has a large developer community. C++ is also very popular in the wider field of artificial intelligence (AI), making it a good option for ML development.

One key differentiator to keep in mind is the speed of C++. Widely considered among the fastest programming languages, C++ is a good candidate where the absolute best performance is critical, such as embedded applications with minimal resources, as well as performance-intensive applications like games.

Lisp

As one of the oldest programming languages, Lisp quickly became a popular choice for AI development. To this day, it continues to be a top choice for cognitive research projects, as scientists attempt to study and model human cognition.

That being said, Lisp is not used as much as other languages for ML development. However, given its strong ties to AI research, it’s not a bad language to learn for use in bigger projects that involve a broader AI focus.

Machine Learning: An Open Field

ML continues to be an open field for developers to make their mark. Companies large and small, in a variety of industries, are increasingly relying on ML for automation, AI development and more.

Of the languages listed above, Python, Java and C++ are the strongest candidates.

For Python developers, the road ahead is clear: Python is already the most popular language for ML development and will likely continue to hold that title indefinitely. Python’s simplicity, ease-of-use, large developer base and powerful libraries make it ideal for the task, and it is unlikely any other language will unseat it.

The same is true for Java. An established developer base, wide popularity and use, as well as powerful libraries make it a good choice for ML development.

C++ is the obvious choice where speed and resource consumption matters more than anything else.

The right language can go a long way toward helping a developer make the most of a career in ML research and development, and these languages are a step in the right direction.

TagsMachine LearningProgramming LanguagesAI
Matt Milano
Technical Writer
Matt is a tech journalist and writer with a background in web and software development.

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DevelopersJune 17, 2020
What Are Some of the Top Machine Learning Languages and Why?
A career in machine learning development offers enough challenges and rewards for a lifetime.

Machine learning is an exciting field for developers. It offers a promising career at the forefront of computer research. To make the most of it, however, it’s important to invest in the right language for the job.

What Is Machine Learning?

Machine learning (ML) is a subset of artificial intelligence aimed at helping computer systems become more intelligent. ML algorithms continually improve with experience, eliminating the need to manually update them with new parameters.

Email filtering that learns with each true or false positive match is a good example of basic machine learning. According to estimates, by the end of 2023, the number of emails sent per day will reach 347 billion. With that many emails, it’s simply not realistic to rely on manual filters to stay ahead of junk mail. Instead, ML algorithms can improve the quality of filtering with a relatively small amount of time spent training them.

Social media recommendations are another example. As a user continues browsing stories and photos, the ML algorithms learn what a user likes and makes recommendations based on them.

The Best Languages to Use For ML Development

In order to make the most of a career in ML development, it’s important to choose the right language. There are a number of languages that are ideally suited for the field.

Python

Python is one of the premier ML development languages. Python started as a hobby of Guido van Rossum in 1989, before being officially released in 1991. The language has become one of the most popular languages in the world, consistently ranking in the top ten since 2003.

One of the things that makes Python so popular is its simple syntax. It is relatively easy to learn, compared to other programming languages. In addition, Python’s entire design philosophy emphasizes simple code and straightforward solutions.

What’s more, van Rossum created Python to be extensible. As a result, the language is very modular, designed around a small, central core, with its features and abilities augmented by a vast library of packages and tools.

Some of those libraries are specifically aimed at ML development. In addition, Python is popular among universities, where vast amounts of ML research is done and many breakthroughs occur.

All of this combines to make Python the language of choice for ML development.

Java

Java is another popular language for ML programming. Like Python, it consistently ranks as one of the most popular programming languages in the world.

Because Java was designed around the principle of Write Once, Run Anywhere (WORA), it’s an ideal language for the development of ML applications across platforms. In addition, like Python, it has a number of libraries to assist with ML development.

Because of its popularity, Java has a thriving developer community, which can be a big help for developers.

R

The R programming language is another popular choice for ML development. Originally released in 1993, it has gained in popularity with data scientists, data miners, statisticians and ML developers.

R runs on Linux, macOS and Windows, furthering its popularity.

C++

Like Java and Python, C++ consistently ranks as one of the most popular languages in use. Like the other languages, C++ has a number of libraries to aid in ML development.

C++ also runs on virtually every platform and has a large developer community. C++ is also very popular in the wider field of artificial intelligence (AI), making it a good option for ML development.

One key differentiator to keep in mind is the speed of C++. Widely considered among the fastest programming languages, C++ is a good candidate where the absolute best performance is critical, such as embedded applications with minimal resources, as well as performance-intensive applications like games.

Lisp

As one of the oldest programming languages, Lisp quickly became a popular choice for AI development. To this day, it continues to be a top choice for cognitive research projects, as scientists attempt to study and model human cognition.

That being said, Lisp is not used as much as other languages for ML development. However, given its strong ties to AI research, it’s not a bad language to learn for use in bigger projects that involve a broader AI focus.

Machine Learning: An Open Field

ML continues to be an open field for developers to make their mark. Companies large and small, in a variety of industries, are increasingly relying on ML for automation, AI development and more.

Of the languages listed above, Python, Java and C++ are the strongest candidates.

For Python developers, the road ahead is clear: Python is already the most popular language for ML development and will likely continue to hold that title indefinitely. Python’s simplicity, ease-of-use, large developer base and powerful libraries make it ideal for the task, and it is unlikely any other language will unseat it.

The same is true for Java. An established developer base, wide popularity and use, as well as powerful libraries make it a good choice for ML development.

C++ is the obvious choice where speed and resource consumption matters more than anything else.

The right language can go a long way toward helping a developer make the most of a career in ML research and development, and these languages are a step in the right direction.

Machine Learning
Programming Languages
AI
About the author
Matt Milano -Technical Writer
Matt is a tech journalist and writer with a background in web and software development.

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