Programming Languages for AI
- (Harvard University - Joyce Yang)
- AI Programming Languages
There are quite a few AI programming languages, and there is none of them that can be called “the best ai programming language.” They all have their pros and cons, and following are some of them: Python, Scala, Java, R, Javascript, Lisp, C++, and Prolog
R and Python both share similar features and are the most popular tools used by data scientists. Both are open-source and henceforth free yet Python is structured as a broadly useful programming language while R is created for statistical analysis.
Python is a programming language that is preferred for programming due to its vast features, applicability, and simplicity. The Python programming language best fits machine learning due to its independent platform and its popularity in the programming community.
Please refer to the following for more details:
- Wikipedia: Python
- Python Website
- W3 School: Python Tutorial
- Wikipedia: R
- R Website
- W3 School: R Tutorial
- Python
The Python programming language was developed in the late 80s and plays a crucial role in powering the internal infrastructure of Google.
Python comprises of enthusiastic developers and now it’s been used in the widely used applications of YouTube, Instagram, Quora, and Dropbox. Python is broadly utilized over the IT business and permits simple effort of collaboration inside development groups.
In this way, in the event that you need an adaptable and multi-reason programming language with a supporting enormous network of engineers alongside the extendable AI bundles then Python is a top pick.
- R
R was developed by statisticians and basically for the statisticians which any developer can predict the same by looking at its syntax. As the language contains mathematical computations involved in machine learning which is derived from statistics, R becomes the right choice who wants to gain a better understanding of the underlying details and build innovative.
If your project is heavily based on statistics then R can be considered as an excellent choice for narrowing down your projects which requires one-time dive into the dataset. For instance - if you like to analyze a corpus of text by deconstructing paragraphs into words or phrases to identify their patterns then R is the best choice.
- Python vs. R
R and Python both share similar features and are the most popular tools used by data scientists. Both are open-source and henceforth free yet Python is structured as a broadly useful programming language while R is created for statistical analysis.
The Python programming language was developed in the late 80s and plays a crucial role in powering the internal infrastructure of Google. Python comprises of enthusiastic developers and now it’s been used in the widely used applications of YouTube, Instagram, Quora, and Dropbox. Python is broadly utilized over the IT business and permits simple effort of collaboration inside development groups. In this way, in the event that you need an adaptable and multi-reason programming language with a supporting enormous network of engineers alongside the extendable AI bundles then Python is a top pick.
R was developed by statisticians and basically for the statisticians which any developer can predict the same by looking at its syntax. As the language contains mathematical computations involved in machine learning which is derived from statistics, R becomes the right choice who wants to gain a better understanding of the underlying details and build innovative. If your project is heavily based on statistics then R can be considered as an excellent choice for narrowing down your projects which requires one-time dive into the dataset. For instance - if you like to analyze a corpus of text by deconstructing paragraphs into words or phrases to identify their patterns then R is the best choice.
