Top 6 JavaScript Machine Learning Libraries You Must Know in 2021

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Machine Learning is, undoubtedly, one of the most talked-about technologies of today’s date. No wonder you would be eager to start working on a Machine Learning project as soon as you get the opportunity. However, it is always better to get your hands on the necessary resources before you begin working on such important projects. You might check assignment help for more information


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Python may be the go-to language for Machine Learning projects, but JavaScript has many offers to such projects. While it plays a crucial role in web development, developers can actually use JavaScript for connecting Machine Learning features to a particular webpage or application. In addition, several useful Machine Learning libraries in JavaScript will be useful in your Machine Learning project.

  1. STDLib:

This one is an open-source library and is powered by JavaScript and Node.js. It offers browser support for scientific and numerical web-based machine learning applications. This library comes with a number of comprehensive and advanced statistical and mathematical functions to help you build high-performing Machine Learning applications and models.

This library can be further used for plotting and graphics functionality for various purposes, including data analysis, data visualisation and more. Some of the crucial features of this library are:

  • 35+ probability distributions for handling probabilistic data
  • 150+ special in-built math functions including limit-continuity, integration, differentiation, data analysis, etc.)
  • 50+ sample dataset for conducting tests and development.


  1. js:

This is yet another open-source Machine Learning JavaScript library. One of the most interesting things about this JavaScript library is that it’s owned by Google. It is the successor of Deeplearn.js. However, Deeplearn.js has become obsolete today.

You can use TensorFlow.js for various purposes, including training the neural network in the application, developing Machine Learning models and application, diagnosis of disease, and more. You can also import the pre-existing trained models to the browser. It automatically provides support for WebGL and Node.js.


  1. js:

This one is particularly useful for beginners. Since Machine Learning concepts often seem difficult for beginners to understand, they often may get discouraged in pursuing such projects altogether. With Brain.js, which is an openly accessible JavaScript library for Neural Networks, you can perform the process of defining, training and running neural networks quite smoothly.

Brain.js can be used in Node.js and also provides browser support and various types of networks for different tasks. If you are new to the Machine Learning thing, Brain.js can be quite useful for you as a JavaScript developer.

Use the following codes to set up Brain.js:

npm install brain.js

Also, you can use the code mentioned below to add the library in the browser:

<script src=””></script>

However, if you want to install the Naïve Bayesian Classifier, you need to use the following code:

npm install classifier


  1. js:

This available open-source framework allows you to run Machine Learning models in the browser. It allows you to run Machine Learning models in the browser. You can even use it to run Keras models in the browser quite easily with the support of GPU via WebGL. If CPU mode is allowed, these models also work in Node.js.

Keras.js also offers support for model training using any backend framework, such as Next.js, Microsoft Cognitive Toolkit (CNTK), Metero.js, etc. There are several Keras models which can be deployed on the client-side browser. Some of them are 50-layer Residual Network (trained on ImageNet), Inception v3 (trained on ImageNet), and Convolutional variation auto-encoder (trained on MNIST).

Here are some of the Keras models which you can run in the browser:

  • Convolutional variation auto-encoder
  • DenseNet-121
  • Inception v3


  1. ConvNetJS:

This JavaScript library is used for the training of Deep Learning models (Neural Networks) in your browser. It is absolutely easy to use. It does not require any special software, compilers, installations, GPUs and others. Just use the following code to install the ConvNetJS:

npm i convnetjs

Beginners may find this library difficult to use, and it is complex and hard to manage. In order to use this library, you need to have common knowledge of this field. Also, the processing may become slower in this when compared to other tools equivalent to this.


  1. js:

This JavaScript library offers Machine Learning tools for working with NodeJS and browsers. The main objective of ML.js is to make Machine Learning more approachable for students, well-skilled coders, and large geographical users. This includes almost every possible algorithm that a developer may need to build good machine learning models.

ML.js gives us the ability to take machine learning to the next level. ML Hub-Team prefers working with new technologies and the great implementations of them. You can use the following code to set up the ML.js tool:

<script src=””></script>

ML.js supports all the Machine Learning algorithms mentioned below:

Supervised Learning:

  • Decision tree: CART
  • K-Nearest Neighbor (KNN)
  • Logistic regression
  • Multi-variate linear regression
  • Naïve Bayes
  • Partial least squares (PLS)
  • Random forest
  • Simple linear regression

Unsupervised learning:

  • K-means clustering
  • Principal component analysis (PCA)



Even though JavaScript is not that closely associated with subjects like Machine Learning and Deep Learning, trends suggest that it will become the most prominent language among Machine Learning developers in the coming years.

Starting your data science journey with JavaScript will certainly be a great advantage for beginners. In fact, it is a great approach for programmers to make progress in the field of Machine Learning.


Author bio: Eric jones is a web developer who is currently working for a reputed eCommerce company based in Australia. He is also a part of the team of experts at, where he offers Java assignment help to students on request.


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