JS is the most popular scripting language for web development with a mature Node Package Manager (npm) ecosystem.
JS is a general purpose, cross-platform programming language
Another benefit of using JS for Machine Learning and Data Science is the language’s universality. The modern web browser is basically a portable application platform that lets you run your code on any device basically without any modification. Tools like electron allow developers to seamlessly build and deploy downloadable desktop applications to any operating system. Node.js allows you to run your code in the server environment while React Native brings your JS code to the native mobile application environment, and can also allow you to develop desktop applications too. JS is no longer confined to web development alone, it has become a general purpose, cross-platform programming language that you can use for Machine Learning and Data Science too.
JS makes Machine Learning accessible to web as well as front-end developers
Using JS makes Machine Learning accessible to web and front-end developers, a group that has been historically left out of the Machine Learning and Data Science debate. Server-sided applications are generally preferred for Machine Learning tools since all the computing powers are in the servers. Improvement in the hardware has made it possible for complex Machine Learning models to be run on the client, be it in the desktop or mobile browser.
TensorFlow.js is JS for Machine Learning library that comes with a comprehensive and flexible range of tools, libraries, and resources. You can run already available TensorFlow models or convert your Python models too. It also comes with pre-existing ML models that you can retain using your own data and deploy anywhere including the browser, the cloud, on-premises or on the device regardless of the language of your choice.
JS libraries for Natural Language Processing
nlp.js provides a JS-based natural language utility for Node.js. It offers different functionalities like obtaining stemmers and tokenizers for different languages or guessing the language of a text. nlp.ps can also perform sentiment analysis for various phrases written in a particular language. You can also use nlp.js to classify the intent of any sentence before generating an answer for the sentence based on the intent using the Natural Language Processing Classifier and the Natural Language Generation Manager. nlp.js comes with a native support for 40 languages and is capable of supporting additional 104 languages with BERT integration.
Compromise is another JS library specially designed for natural language processing to make it easier to interpret and pre-parse the text for decision making purposes. You can use it to compress words and expand them at the runtime so that assumptions can be generated. About 99.9 percent of all English vocabulary can be handled by 14,00 words that are compressed into a 40kb file. This makes Compromise quite fast in understanding and scanning words with latency in milliseconds.
JS Libraries for Data Science
Graphs are crucial part of data visualization, and Sigma.js is specially focused on drawing graphs. This library comes with in-built features that simplify graph visualization and publishing your output on the web pages. Sigma.js also has Canvas and WebGL support as well as options for mouse and touch support, added accessibility, custom rendering, and more. You can also modify the data, listen to events, move your camera, and modify the rendering in any manner you need and add extra levels of interactivity with the graphs.