Machine Learning and Artificial Intelligence are on the check list of every aspiring software developer. But a question that everyone has one their mind is, “What programming language should I use for learning Artificial Intelligence and Machine Learning”. In this post we will discuss about the top 5 languages you can use for learning Artificial Intelligence, Machine Learning and Deep Learning along with the reasons and applications for each one of them.
1. Python
There is no surprise that Python has yet again made it to the top as a Programming Language for Data Science, Artificial Intelligence and Machine Learning. Python is an interpreted, high-level, general-purpose programming language created by Guido van Rossum and first released in 1991. Python is a powerful programming language which was designed keeping simplicity and code readability in mind. This combined with the ease of Python is one of the main reasons why it is the first programming language for many people. Besides Python has a huge community support and has libraries for more things you can ever think of.
Over the past few years it has become the language of choice for many people working in the fields of Data Science, Artificial Intelligence and Machine Learning. The libraries of Python such as Numpy, Scipy, Pandas, Matplotlib, Scikit-Learn find their uses in Data Science and Machine Learning, whereas heavyweights such as Tensorflow and Pytorch to name a few are used to train Deep Neural Networks. Python also has many libraries available for Data Scraping, Data Visualization, Computer Vision and others which makes the programming language a very good choice. Further Python also has tools like Jupyter Notebook which help in working with Python and at the same time making it easy to share it with other people and collaborate.
2. C++
C++ is one of the most used programming languages which was created as an extension of C. It is a general-purpose programming language created by Bjarne Stroustrup under the name “C with Classes”. Despite being not as beginner friendly and easy to learn as Python, it still makes up to the second spot, and there are some really good reasons. Firstly, it is a low level programming language- easier for computers to understand but difficult for humans to understand, which makes it to run faster than all interpreted Programming Languages, including Python. Also, it is a static programming language, which means that there won’t be any unchecked type errors showing at runtime.
Both of these make C++ a language of choice for production and deployment of machine learning and deep learning models. In Fact, many of the python libraries for Deep Learning such as Tensorflow and Pytorch have their low level implementation written in C++. The fact that a lot many people know how to program in C++ also makes this language more suited for Machine Learning and Deep Learning.
3. Java
Java is a general-purpose programming language that is class-based, object-oriented, and designed to have as few implementation dependencies as possible. It was created by James Gosling at Sun Microsystems and is one of the most used programming languages in today’s world and has a very large community of developers. Java supports cross-platform applications, and over 5 billion devices run Java, besides all android devices also run java. For these reasons, Java is one of the preferred languages when running Machine Learning or Deep Learning models in applications that work across many platforms, such as android applications. Java also has many libraries that help in creating and implementing Machine Learning and Deep Learning models such as ADAMS(short for Advanced Data Mining And Machine Learning System), Deeplearning4j, Weka and JavaML.
4. JavaScript
JavaScript is a high-level, just-in-time compiled, object-oriented programming language. It is the programming language of the web, all front-end programming of the web is done in JavaScript. And for a long time since its inception that was the only place where it was used, hence it was considered a toy- language by many developers around the globe. However with the creation of V8 engine- an interpreter for javascript developed by Google as a part of the Chrome project, and Nodejs, that thing changed and Javascript quickly came to the server end as a language of choice for many.
However with the release of tensorflow for javascript, the situation has changed again, thanks again to google for that. Machine Learning and Deep Learning models can now be trained and deployed directly in the browser or in Nodejs. Besides, javascript boasts a huge community support which is probably bigger than any other programming language in the world and hence has many other libraries for implementing machine learning and deep learning models and performing mathematical calculations such as Brain.js, Synaptic, Math.js and ConvNetJS to name a few. However Javascript does have the disadvantage of having a steeper learning curve compared to Python because of its asynchronous programming nature. However if you wish to deploy models in browser, you don’t have any other option than to use JavaScript.
5. R
R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. It was initiated exclusively for scientific data by Ross Ihaka and Robert Gentleman in 1995. The R language is widely used among statisticians and data miners for developing statistical software and data analysis, Data Science and Machine Learning.
Though it is one of the most sought after skills in Data Science Jobs, it comes at 5th in our ranking due to a variety of reasons. First it is rarely used in production and deployment, besides it does not have much support and libraries for creating Deep Neural Networks. Also, since R stores all of the data inside the RAM, it is not very convenient to work with Big Data. Nevertheless, it is used extensively in academia, also it is an excellent choice if you require to perform Data Analytics, Data Visualization or rapid prototyping of Machine Learning models.