ML.NET provides an open source, cross platform machine learning framework for .NET applications.
Open source
24 alternativesmachinelearn.js
The machinelearn.js project offers machine learning tools for web applications and Node.js.
About machinelearn.js
machinelearn.js is a Machine Learning library written in TypeScript for the web and Node.js. It handles machine learning problems and is intended to show how machine learning algorithms work.
It provides a consistent set of model methods for training, inferencing, and saving or loading state, and installs through yarn or npm with a browser build distributed via jsDelivr. By default it uses the pure JavaScript version of tfjs, with C++ or GPU acceleration available through separate imports.
Key features
- Works in web browsers and Node.js
- Installs with yarn add machinelearn or npm install
- Loads in browser via jsDelivr script tag
- Uses fit for training and predict for inferencing
- Saves and loads model state with toJSON and fromJSON
- Supports C++ and GPU acceleration via separate packages
Best for: Developers and students who want to use and learn machine learning algorithms in TypeScript web or Node.js projects.
Good for
- Consistent APIs across models for training, prediction, and state handling
- Available for both browser and Node.js use
- Open source under MIT license
Watch out for
- Acceleration requires installing and importing separate C++ or GPU packages
machinelearn.js at a glance
- Category
- Development › Programming Languages
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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machinelearn.js FAQ
How do I install machinelearn.js?
Using yarn with `yarn add machinelearn` or npm with `npm install --save machinelearn`.
How do I enable C++ or GPU acceleration?
Install `machinelearn-node` for C++ or `machinelearn-gpu` for GPU, then import the package.
What methods do models share?
`fit` for training, `predict` for inferencing, `toJSON` for saving state, and `fromJSON` for loading state.
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