machinelearn.js is a Machine Learning library written in Typescript. It solves Machine Learning problems and teaches users how Machine Learning algorithms work.

Using yarn

$ yarn add machinelearn

Using NPM

$ npm install --save machinelearn

On the browsers

We use jsdeliver to distribute browser version of machinelearn.js

<script src="https://cdn.jsdelivr.net/npm/machinelearn/machinelearn.min.js"></script>
<script>
    const { RandomForestClassifier } = ml.ensemble;
    const cls = new RandomForestClassifier();
</script>

Please see https://www.jsdelivr.com/package/npm/machinelearn for more details.

By default, machinelearning.js will use pure Javascript version of tfjs. To enable acceleration through C++ binding or GPU, you must import machinelearn-node for C++ or machinelearn-gpu for GPU.

1. C++

• installation

yarn add machinelearn-node

• activation

import 'machinelearn-node';

2. GPU

• installation

yarn add machinelearn-gpu

• activation

import 'machinelearn-gpu';

Machinelearn.js Bot will help you understand this repository better. You can ask for code examples, installation guide, debugging help and much more.

• Machine Learning on the browser and Node.js
• Learning APIs for users
• Low entry barrier

We welcome new contributors of all level of experience. The development guide will be added to assist new contributors to easily join the project.

• You want to participate in a Machine Learning project, which will boost your Machine Learning skills and knowledge
• Looking to be part of a growing community
• You want to learn Machine Learning
• You like Typescript ❤️ Machine Learning

machinelearn.js provides a simple and consistent set of APIs to interact with the models and algorithms. For example, all models have follow APIs:

• fit for training
• predict for inferencing
• toJSON for saving the model's state
• fromJSON for loading the model from the checkpoint

Testing ensures you that you are currently using the most stable version of machinelearn.js

$ npm run test

Simply give us a 🌟 by clicking on

We simply follow "fork-and-pull" workflow of Github. Please read CONTRIBUTING.md for more detail.

Great references that helped building this project!

• https://machinelearningmastery.com/
• https://github.com/mljs/ml
• http://scikit-learn.org/stable/documentation.html

Thanks goes to these wonderful people (emoji key):

Jason Shin 📝 🐛 💻 📖 ⚠️	Jaivarsan 💬 🤔 📢	Oleg Stotsky 🐛 💻 📖 ⚠️	Ben 💬 🎨 📢 🐛 💻	Christoph Reinbothe 💻 🤔 🚇 👀	Adam King 💻 ⚠️ 📖