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The Top Five Languages Paving The Future Of Machine Learning

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The Top Five Languages Paving The Future Of Machine Learning by Mauricio Prinzlau

The future of machine learning seems to be very bright, with leaping advances in software & technology and the proliferation of the cloud. It is currently one of the fastest emerging technologies in the world, with many experts claiming that it holds the key to unlocking the doors to computing’s most mystical evolution- artificial intelligence. But sci-fi concepts apart, machine learning is a powerful tool that is already being used to solve complex classification problems.

But as machine learning systems continue to evolve, they will be an increased demand for smarter languages that will be able to process a number of complex issues and general paradigms, some of which might be too complicated for humans to process. With the industry’s growing experience with smart machine learning systems, entire field of machine learning is being staged to shift from simple problem solving to the creation of powerful and complex algorithms that work based on advanced-level. That being said, a number of machine learning languages have already paved the way for the future of integration and artificial intelligence. Let’s take a look at the top 5 machine learning languages that are paving the way how future systems will be developed:

Languages Paving The Future Of Machine Learning – MATLAB/Octave

MATLAB is already being used by millions of software engineers and developers to assess, analyze and develop cutting edge systems and products that are changing our very way of life. MATLAB is a matrix-based language that has emerged as the most simplistic method of demonstrating computational mathematics. With built-in graphics and comprehensive development environments, MATLAB makes it easy for analysts and developers to visualize data and gain meaningful insights from it. It is a platform that offers the ideal desktop environment for developers to experiment, explore, and discover new algorithms and methodologies. Apart from that MATLAB’s tools are tried and tested language whose computing capabilities have been designed and tested for durability.

Overall, MATLAB is the perfect machine learning language for representation and execution of matrices. It also offers the perfect platform to use when divulging into linear algebra of given methodologies. The language is well to self-learn about algorithms as a brush over, as well as comprehensive understanding when trying to understand complex methods. It is currently a very popular language in numerous beginner-level university courses. MATLAB’s suitability for complex matrix operations is supplemented by detailed feature matrix.


R has been the statistical analysis workhorse quite some time now, as well for machine learning, to a certain extent. The R machine learning language is meant for the advanced user because of its complex nature and wide learning curve. That being said, it is the perfect platform for those looking to comprehend and explore statistical data via graph. R possesses a nearly infinite number of machine learning algorithms and complex implementations, all written by the algorithm developer. It is a competent language for those who want to discover, design and test algorithms. It is best suited for one-off projects that comprise of artefacts, such as reports, research papers, or even predictions. R is currently the single most popular machine learning platform for competitors, like Kaggle, as they prefer it over other languages like Python.

R offers more in terms of data analysis and algorithm options and is also a very powerful tool thanks to the numerous machine learning algorithms that are present by default, making it a future ready machine learning language. The only possible issue is that all of R’s algorithms are created by third parties vendors, which can often lead to inconsistent developer experience. This can affect productivity as the developer has to learn how to model data, as well as repeatedly predict each algorithm, in every package.


The Python machine learning language is a data science book that has been in use with the manufacturing industry for a while now, and is routinely being used to bring production systems into operations. Python is a class-leading machine learning system that gives users direct access to predictive analytics, therefore proving itself to be the world’s foremost data science languages. It has become the machine language of choice for developers who are looking to frame better questions or expand the capabilities of their existing machine learning systems.

Python is a comprehensive language that covers a range of libraries, including those of Teano, Keras and scikit-learn. It also features easy to comprehend walkthroughs and even useful tips from opinion analysis to neural networks, allowing users to find answers to complicated issues. Python is one of the increasingly popular scientific languages, and its user friendliness only adds to its appeal. Python is also a useful communication tools that takes us one step closer to a future of reproducibility. On the down side, Python is comparatively more fragmented than other machine languages, which can drastically reduce productivity.

The Top Five Languages Paving The Future Of Machine Learning


Machine learning is a sequence of complex algorithms and not mere black magic, and the C-family machine learning language is the perfect example of how good design and user-centric features can automate sequences. For consequential production implementations, C offers users a robust library that allows them to customize implementations of project-specific algorithms.

The Java/C-family machine learning language is a haven for the seasoned developer who has the time to make minor tweaks using comprehensive libraries. It is no surprise then that most current and older machine learning algorithms are written in Java. Even deep learning implementations for LIBLINEAR and LIBSVM are written in C-family language, with Python and other tools used as leverage. Java is a functional programming language that will allow future machine learning systems with speed, accuracy, and precision.

Extreme Learning Machines (ELM)

ELM is a highly-functional programming language that allows users to effortlessly create browser-based user interfaces. Elm uses a utilitarian programming style and graphical layout that is highly reactive, allowing users to design interfaces without any disastrous updates. Programming languages have a typically short lifespan, but Extreme Learning Machine (ELM) language is one language that has a bright future ahead of it.

In theory, ELM is considered to be a programming language, but for all practical purposes, it can be more closely compared with platforms like jQuery, CoffeeScript, and so on. Extreme Learning Machine (ELM) has great potential thanks to its prospect because of its learning speed, making it the perfect language for big data applications, which are currently dominating the cloud. And as long as the basic issue of noisy data performance is addressed, it’s sure to have an impactful.

About Mauricio Prinzlau

Bio: Mauricio is the CEO of Cloudwards.net, a data and user feedback driven comparison engine for cloud apps and services. He enjoys writing and producing educational videos around the cloud to help people find the best cloud service for their needs.

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