Best JavaScript Libraries for Machine Learning and Data Science

JS libraries for ML and Data Science

JavaScript is the most popular programming languages of the web which makes it very important. However, it has mostly been used as a programming language with web and application development with limited association with Machine Learning and Data Science compared to Python and R. This is because these programming languages are specifically tailored for ML and Data Science thanks to a huge collection of supporting libraries, an active community, and robust infrastructure. However, in recent times, JavaScript libraries for Machine Learning and Data Science have gained a lot of popularity with more and more programmers opting for it.

So, why is JavaScript for Machine Learning and Data Science gaining momentum?

JS is the most popular scripting language for web development with a mature Node Package Manager (npm) ecosystem.

There are some benefits that come with using JavaScript for Machine Learning and Data Science. It’s popularity is one of them. While JS for Machine Learning is not as popular and Python or R for Machine Learning, JavaScript itself is. And as demand for ML applications rise, and as the hardware becomes cheaper and faster, it is only natural that JavaScript for ML and Data Science will become more prevalent.

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.

Best JavaScript libraries for Machine Learning


Brian.js is a JS library for Machine Learning and Neural Networks. It is very fast since it uses GPU for computations and has the unique capacity to revert back to pure JavaScript when GPU is unavailable. Brian.js allows for the implementation of various types of neural networks and the best thing about it is that you do not need to be familiar with neural nets to use Brian.js. You can also import your models as a function or in a JSON format for integration into your website.


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.


Synaptic is a JavaScript neural network library that is created for the browser and Node.js. You can either import or export networks to JSON as standalone functions. You can also connect these networks to other networks or even gate connections. Synaptic also comes with many useful in-built architectures like Multilayer long-short term memory networks (LSTMs), liquid state machines, Hopfield networks, multilayer perceptrons, and more combined with trainers that adapt any type of network and use any training set along with it. Finally, Synaptic is also an open-source library from MIT so anyone can contribute to it.


ConvNet.js is a JavaScript library that is specially dedicated to training deep learning models that include neural networks. One of the major advantages of this library is that you can use it entirely in the browser without the need for any special software requirement like compilers or GPUs. ConvJet.js comes with options for neural networks, classification and regression problems, convolutional networks that focus on images, and the Reinforcement Learning module that is currently in its experimental stages.


ml5.js is a JavaScript ML library that is based on top of TensorFlow without additional external dependencies. With ml5.js, you can access pre-trained ML algorithms in the browser that is used to detect human poses, styling an image with another, pitch detection, text generation, matching English words, composing music, and more. ml5.js focuses on providing a deeper understanding of ML to users along with its complexities like ethical computing, and responsible data collection among others.

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. 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


Data-Driven Documents (or D3) is a JavaScript library that you can use to manipulate the data using HTML, CSS, and SVG to obtain custom data visualizations. You can use D3 to combine documents with a Document object model before transforming the document-based on what you need to achieve. D3 comes with different chart types for data analysis like histograms, box plots, for hierarchies like treemaps, and for networks like chard graphs. You can also use it for common charts like line charts, scatter plots, pie charts, bar charts, and more. D3 also comes with animation options like zoomable, animated treemap, bar chart races, icicles, and bar charts.


Chart.js is an open-source JavaScript charting library that offers 8 broad chart types that include all the common charts like pie charts, bar charts, scatterplots, histograms, error charts and more. All the charts can be combined to customizable mixed charts that can also be animated. Chart.js can also be seamlessly rendered across all the web browsers and charts adjusted according to your browser’s window size. You can also combine the charts in this library with the Movement.js library if you need a time axis.


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.


There you have it! These are the best JavaScript libraries for Machine Learning and Data Science worth learning. While JS is not as popular for ML and Data Science as Python and R, it is becoming increasingly popular these days. So be sure to check out these libraries, and who knows, you might find them handy for your next ML or Data Science project.

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Advanced Machine Learning with TensorFlow on Google Cloud Platform
End-to-End Machine Learning with TensorFlow on GCP
Data Pipelines with TensorFlow Data Services
Sequences, Time Series and Prediction
Natural Language Processing and Capstone Assignment
Natural Language Processing
Information Visualization: Programming with D3. js
Information Visualization: Advanced Techniques

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