Best Programming Language for Machine Learning

Best Programming Language for Machine Learning

If you are looking for the best programming language that you need to know for a career in machine learning, this article will tell you all about it.

Programming languages are like the bones with which the entire skeleton of machine learning and Artificial Intelligence is formed. As we know, computers aren’t designed to understand the syntax of the ordinary languages of our speech. To be able to communicate with a computer, and to work with it, we have to make use of computer languages or, in this case, programming languages.

Just like different bones in our body have different structures and perform different functions, there are many different kinds of programming languages, each of which is capable of meeting certain specific needs.

The vastness of available options when it comes to choosing a programming language often confuses developers. When a person enters the field of machine learning, especially in the initial stages, she is often perplexed by the question regarding which is the best programming language and which language she should learn. Such confusions aren’t limited to amateurs but, at times, are faced even by advanced developers.

Looking for ‘The Best’ – The Problem at the Root

I know you came here looking for ‘the best programming language’ and I find it necessary to break this spell at the outset. By using the analogy of bones and skeletons in the earlier paragraphs, I intended to point out the fact that programming languages are purpose driven.

While ordinary languages are expansive enough to enable its users to communicate holistically, programming languages are designed to meet specific computational demands. This can be regarded as one of the fundamental differences between ordinary languages and programming languages. However, the fact remains that a major stream of research in computer sciences focuses on designing computer languages which are akin to ordinary languages in quality and quantity.

Anyway, this shows us that it’s a mistake to look for ‘the best’ programming language as each may be the best in its specific domain. Suppose, a programming language is designed for engineering projects. Even though it may be the best language for this purpose, it may not be usable for web-development, which, has yet another ‘best’ language.

So, instead of limiting our search to the singular sense, let us explore and discuss ‘some of the best’ programming languages which you can learn and use.

Top 5 Programming Languages for Machine Learning

To reiterate, the reader must note that this is not a vertical list which is hierarchically arranged but rather a horizontal one. In fact, these of some of the most commonly used programming languages, which together solve the various problems of the field of Artificial Intelligence.

Python

If you ask me the best thing about the Python language, I’d say that it’s the simplicity of its syntax and the consequent ease of action. Python is indeed a very versatile language, which incorporates various styles of programming, including the object-oriented, procedure-oriented and functional styles.

Plus, in comparison with other languages like Java or C++, developers need lesser time to construct complex AI algorithms in Python. Another significant up-side of the language is that it involves a number of libraries which support the coding process. For instance, one might consider Pybrain which is a Python library, specifically designed for machine learning projects.

R

If the need is to analyze and manipulate statistical data, R is the best programming language framework for this purpose. In order to design well-structured publication-quality plots which require the inclusion of semantic notations, mathematical or otherwise, R is the go-to choice for developers.

Despite being more of a general-purpose language, the R framework comes with a variety of packages like RODBC, Class, G-Models and TM, all of which tremendously enhance the process of machine learning development. In fact, proper implementation of machine learning in business environments often remains incomplete without the use of R.

Lisp

John McCarthy, popularly referred to as the Father of Artificial Intelligence, developed this language back in 1958. Obviously, this is the oldest language which paved the foundations to AI as we see it today.

Apart from the ability to use symbols, this language is better known for its ability to create prototypes, dynamic objects, as well as, to collect bad elements or garbage automatically. Moreover, changes can be made into the algorithm while the program is running, and this allows for a heightened interactive development process. With time, however, the unique features of Lisp have been included in other programs, resulting in the waning fame of this oldie.

Prolog

Featuring alongside Lisp in the course of developments in AI, Prolog is extremely adept at matching patterns, automatic backtracking and tree-based data structuring. At present, this language is mostly used for developing AI-based programs in the field of medical science and technology. Yet, its use is not limited to this domain and developers often use Prolog for developing machine learning programs in various other fields.

Java

The old and the faithful, Java, is also widely used for developing AI-based programs. Mostly, Java is used to write search algorithms, as well as, to develop artificial neural networks, both of which play pivotal roles in AI development. Apart from these, the language is also commonly used for Genetic programming.

Some of the major benefits of using the Java framework include simplicity, ease of debugging, heightened user-program interaction and efficient graphical representation of data. Alongside, Java also enables package services which are very helpful in AI programming. With tools like Swing and the Standard Widget Toolkit (SWT), Java allows developers to create seamless graphics and digital interfaces for their projects.

Conclusion

In all, different programming languages are better suited for certain programming needs. Some, like Python, offer simplicity while others, like R, allow us to effectively handle statistical data. Now, to finally answer the question of which language you should use, I’d leave you by saying that it completely depends on the needs of your project. As a learner, it’s always better to have most, if not all, of these programming languages in your repertoire.

Prateek Arora

Contributing Editor at Wimoxez. Apart from this, I'm big into books and love reading books in different niche.

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