Major tech giants have been investing heavily in Machine Learning (ML) to develop products and improve efficiency. Machine learning has several very practical applications in many fields including business. It is useful for saving money, time and giving a new a dimension to the future of your organization.
There is a huge demand for skill in ML of late, due to the demand and practical implementation of the ML in every sector. In order to excel in the machine learning, programmers must be well versed with programming languages. There are many languages those would fit in the arena of machine learning. In this article, we highlighted four major programming languages for practicing machine learning.
Machine learning is a subset of artificial intelligence (AI) that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. Arthur Samuel coined the name in 1959.
Python is a popular and dynamic programming language. Since the inception in the year 1991, the popularity of the programming language has been increasing across the globe. Developers have been preferring the language as it is open source and simple syntax for easy coding. For machine learning, Python is the best language for the beginners. The specific libraries like numpy and scipy enable the computer to learn linear algebra and kernel methods for machine learning.
Lisp is the second-oldest high-level programming language and most suitable language for the development in machine learning. The name LISP derives from "LISt Processor” and it was invented by John McCarthy, the father of Artificial Intelligence, in 1958. The language has a feature of processing the symbolic information effectively.
The programming language R is supported by the R Foundation for Statistical Computing. It is one of the perfect environments for statistical computing and graphics. It is not wrong to say that it’s widely used by statisticians and data miners across the world to develop statistical software and data analysis. One can create useful algorithms by using R studio and it will be a proper language for practicing the machine learning.
Having expertise in one of these languages could be useful for tech companies and the developers to practice and implement their work in machine learning.