10 Most Used Programming Languages in Machine Learning Development

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Like AI development, the creation of machine learning products and software has become increasingly popular over the years. Machine learning is referred to as a process of using algorithms, statistics, probability, and other math-based tools to alter or organize data. This area of the tech industry works best with predictive analyses, recognizing patterns, and other ways of viewing or connecting data.

The three main types of machine learning are:

  • Reinforcement
  • Unsupervised
  • Supervised

Some of the most popular applications of machine learning include translating speech, facial recognition, recommendations of products, self-driving cars, social media analytics, and fraud detection. Machine learning applications are used by many major corporations like Google, Facebook, Twitter, Apple, Microsoft, IBM, and Yelp.

Here are the 10 most used programming languages that are used in machine learning development:

  1. Scala

Compatible with Java applications, compiled, statically-typed, and can support large amounts of data, Scala has many advantages.

  1. Python

Often compared with Java, Python has a simpler syntax and is easier to learn. Similarly, it’s very widely used and has a fast processing speed.

  1. Lisp

Lisp is one of the oldest programming languages and has a solid foundation as a programming language. It also is domain-specific, contains an automatic garbage collector, and is great for developing prototypes.

  1. Java/JavaScript

Highly versatile, popular, and backed by an extensive community of developers, Java and JavaScript are perhaps the most widely used programming languages. They are object-oriented, can support complex systems, and are supported across numerous platforms.

  1. TypeScript

TypeScript was developed by Microsoft, meaning it has experienced developers keeping it updated and running smoothly. Additionally, it supports Java-based code and systems as well as being considered a simpler version of JavaScript.

  1. Shell

Shell has a simple syntax that works well when developing algorithms, models, and applications with machine learning development projects. It contains a user interface, supporting many operating systems too.

  1. Golang

Commonly just called Go, this programming language is open-sourced and lightweight. Its concurrency feature is its biggest asset, but it also has a built-in vocabulary.

  1. C/C++

C and C++ are called more traditional languages and fall in the same category as Java in that respect. They are easily readable, popular among developers, and maintain quick execution speeds.

  1. Julia

Model analytics is Julia’s specialty due to its dynamically-typed structure and easy to learn syntax. It supports scripting and is cross-platform available as well. Being free and open-sourced are more advantages to using this language.

  1. R

R is referred to as both R and R Programming. Like most of the programming languages on this list, R is open-sourced. It’s backed and supported by developers, particularly those who work with data and statistics. A heavy computing functionality, wide uses in the biomedical field, and support of popular operating systems makes R very popular.

Final Thoughts

Finding programming languages that are open-sourced and versatile across different platforms are some of the most important characteristics of a good language. How these languages handle data, as well as their specialties, allow developers to best compare them on a project basis—a vital decision that could effect the entire development process.