Top 7 Best Programming Languages for Machine Learning ELEKS: Enterprise Software Development, Technology Consulting

To discover how Inoxoft can assist you on your AI journey and leverage the expertise of our software development professionals, please contact us. 15+ years managing app processes, workflows, prototypes, and IoT innovation and hardware for over 500 projects. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. The term “artificial intelligence” was first coined in 1956 by computer scientist John McCarthy, when the field of artificial intelligence research was founded as an academic discipline. Artificial intelligence is one of the most fascinating and rapidly growing fields in computer science.

Scala offers access to BigDL, a distributed deep learning library that seamlessly integrates with popular big data frameworks like Apache Spark. Deal with numerical processing efficiently using Breeze, a powerful library for numerical computing. Java has a steeper learning curve than Python (though not as steep as C++). Writing programs in Java also takes more time than Python, and those programs often require more debugging due to their complexity. These are speed bumps that slow down rapid prototyping of machine learning models. Finally, Java’s community isn’t as active as Python’s when it comes to developing AI-focused tools, and as a result Java is useful for a narrower range of machine learning and data science tasks.

Is Python the best language for AI?

Therefore, it is important to proactively source candidates from a variety of channels, rather than solely relying on applicants to respond to job postings. Some of the sources that tech recruiters use to find candidates include GitHub, GitLab, LinkedIn, Telegram, Discord servers, and consultancy companies. An appealing and accurate job description can better attract targeted candidates, providing you with a more qualified list of applicants.

Specifically, coders can use a previously established value to compute a new one. With structured programming, alternatively called modular programming, code is readable and there is leeway for reusable components, which most developers find helpful. Structured programming is a programming paradigm that favors one, singular structure per program. In this way, code executes instruction by instruction chronologically.

Java: Bridging Scalability and Versatility

NLP tasks, such as parsing and semantic analysis, benefit from Haskell’s strong typing and expressive features. The language’s ability to handle complex data structures and patterns makes it a solid choice for developing robust NLP applications. Haskell is well-suited for implementing and experimenting with machine learning algorithms. The language’s functional nature allows developers to express complex algorithms more succinctly, enhancing the readability and maintainability of the code. R boasts built-in functional programming capabilities, allowing developers to write code in a more modular and efficient manner. This feature makes it easier to implement complex algorithms and models in AI development.

Lisp is the second-oldest high-level programming language and is only surpassed in age by Fortran. Yet, despite its age, it’s highly extensible, with the ability to effectively define your own syntax and create domain-specific languages. The performance of Java is another strength, with just-in-time compilation offering speed close to lower-level languages like C++. Its extensive standard library provides functionality for a broad range of tasks without requiring external packages. A comprehensive library for machine learning, Scikit-learn provides a collection of supervised and unsupervised learning algorithms.

Top 10 Programming Languages for AI Development in 2024

Read more about Best Programming Languages for AI here.