Best Programming Languages For Machine Learning

If you’re a beginner in the field of machine learning, the most difficult segment of the machine learning field is -How to begin with it? Whether you are just focusing on progressing in your machine learning skills or completely making a career transformation into the machine learning domain, it is certain to think which language is the best to learn. There are hundreds of programming languages utilized widely, and each of those languages has its pros and cons, discovering which is the best language for machine learning is surely a tough task. Despite all the confusion, there is a good thing to consider when deciding about the best language to learn for machine learning. You just need to discover which programming language will be most suitable for a business problem you will be solving or the project-focused requirements of machine learning you are going to dive into. Those who want to make a career in Artificial intelligence and machine learning can learn programming languages which are necessary to progress in the niche area of machine learning or specific interests.

In brief, There are tons of programming languages available but you will have to choose the most suitable language for your prospects in the AI and Machine Learning field.


Programming Languages For Machine Learning

1. Java

Java is one of the most used languages by machine learning developers and data scientists, a significant number of these experts use it for network security/computer-based attacks and fraud detection where python is not used most of the time. Java is popularly known as an easy-to-use language that has a debugging procedure that is extremely simple and basic, consists of huge package service, work simplification in mega projects, representation of data in a graphical manner, and superior user interaction. The latest version of java has enhanced features for machine learning and AI programming. It is known throughout the industry as a secure and safe language as it uses bytecode and sandboxes. As a result, All the previous and latest machine learning algorithms are written in Java. Easily called a functional programming language that will authorize all the respective future machine learning systems with accuracy and precision. Undeniably, you need to learn Java, if you are going in an enterprise background.




Python is ranked as the top language by the IEEE spectrum in its latest list of top programming language rankings.

Even in the Stack Overflow rankings, it is mentioned that Python is the only language to rise in-demand for the past several years.

The simple nature of this wonderful language has made it a go-to language for AI. Compared with other similar programming languages, Python can be learned quickly and reduces the time required for development, while maintaining support for functional, object-oriented, and also procedure-oriented programming.

Python has an available set of multiple code libraries, such as Numpy dedicated towards calculation and Pybrain focused on machine learning, lowering the amount of time required to program in Python. If working with Images you can use Scikit and Opencv. If you are using audio then use lebrosa, for the implementation of deep learning you can use TensorFlow, Keras, PyTorch, scientific computing usage is successful with the use of  Sci-Py, for data visualization utilize Matplotlib, Sci-Kit, and Seaborn.

Being a hugely popular language to learn, aspiring programmers start their programming careers by learning Python. There are many tutorials, resources, and online lectures available so even if you are critically stuck while learning, there are many places to find out the solutions.


3. R Language

R language is known as a well-known selected language for analysis, manipulation, and evaluation of statistical data. It can also be used by non-programmers including data analysts, and statistics professionals. Targeted towards those people who want to explore statistical data via graphs, it is the ideal platform. This language is also utilized for a variety of purposes by data scientists at large corporations such as Facebook, Google, and many others. R language is widely known as a general-purpose programming language but many packages are available that make R Language chiefly well suited for AI and machine learning tasks such as regression, and decision tree formation and classification. The packages used for machine learning tasks are RODBC, Gmodels, Class, and Tm. The main benefit of using these packages is to simplify the procedure of implementing algorithms for machine learning, specifically for business-related problem-solving.

Also, this language is suitable for single-time projects such as large reports, research journals, and papers.

Here are some of the list of packages for machine learning which R offers:

  • MICE for Missing values Deal

  • Rmarkdown and Shiny for Communication insights

  • Ggplot2 for Visualization creation

  • RandomFOREST for Decision trees creation

  • dplyr and tidyr for data manipulation

  • CARET for classification and regression issues

  • PARTY for creation of data partition


4. Lisp

This is a somewhat less famous language when compared with the other languages in the list here, Lisp was founded in 1958 by John McCarthy, famously known as the father of Artificial Intelligence. It is the second-oldest high-level language after Fortran to be extensively used today. Lisp has a large number of advantages that make it convenient for AI development. It is excellent for prototyping and also makes way for the simple and effective dynamic creation of objects.

As Lisp language is better at adapting to a programmer's solutions, the first AI chatbot named ELIZA was developed with the help of Lisp. Many of today’s chatbots are still developed using this programming language. Lisp is favored in all those AI projects where a heavy usage of machine learning exists.

Lisp offers programmers Rapid prototyping capacity, Dynamic object creation, Automatic garbage collection, Flexibility, symbolic expressions support. As Lisp is an old language, it lacks support for some of the famous machine learning libraries. Lisp is somewhat hard to learn and does not offer huge community support like it’s provided by Python and R.

5. Javascript

JavaScript is a commonly used language for web development. On a web-based dashboard, it helps visualize the results of machine learning algorithms. New Developers into data science and machine learning favor javascript for developing enterprise-centric applications. It is a            high-level, object-oriented, and dynamic interpreted language that is standardized in ECMAScript. It helps to make machine learning usable even by all the non-technical background practitioners by providing easy access to Machine Learning algorithms and models within the browser. With the implementation of NodeJS, javascript has already become one of the high rated programming languages and as even the community of javascript programmers is increasing quickly, NPM repositories are getting even bigger. JavaScript is even helpful for game development and mobile applications. It is known as a scripting language that gives a huge opportunity and scope for learners in the future.


6. Scala

One of the core languages that is highly supported by the Apache Spark platform is Scala. Known to be a comprehensive data platform that offers functionalities for processing of big data and machine learning analysis by its MLlib library. This is the useful language that provides programmers with the development, design, code, and deployment of machine learning algorithms by making the best usage of the capabilities provided by Spark and other usual big data technologies. There are a huge number of highly developed libraries of Scala language that are most appropriate for linear algebra, random number generation, and scientific computing. Saddle, which is a data library backed by Scala, provides a robust foundation for manipulation of data through array-backed support, strong towards missing values, automatic alignment of data, and 2D data structures.



7. Prolog

Prolog is a powerful and flexible programming language often used for AI and Machine Learning. Prolog language is best recognized for the efficiency of its pattern-matching ability, the tree-based data structuring it offers, and the automatic backtracking feature. It is a superb example of supplementary language. Also, it is known to be an excellent “additional” skill for medical specialists as it is most commonly used in the medical field and particularly in AI specialists. While you focus on learning this language you have the privilege of focusing on the basics and not digging deep inside this language to enhance your value in the market.



Ultimately the language you learn depends on your ability to learn and the type of projects you undertake. Though there are several more languages to be considered, these 7 languages listed above are the most used, appropriate, and flexible to start with Machine Learning and AI.