A high-level machine learning and deep learning library for the PHP language.
- Developer-friendly API is delightful to use
- 40+ supervised and unsupervised learning algorithms
- Support for ETL, preprocessing, and cross-validation
- Open source and free to use commercially
Install Rubix ML into your project using Composer:
$ composer require rubix/ml- PHP 7.4 or above
- Tensor extension for fast Matrix/Vector computing
- Extras Package for experimental features
- GD extension for image support
- Mbstring extension for fast multibyte string manipulation
- SVM extension for Support Vector Machine engine (libsvm)
- PDO extension for relational database support
Read the latest docs here.
Rubix ML is a free open-source machine learning (ML) library that allows you to build programs that learn from your data using the PHP language. We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects.
If you are new to machine learning, we recommend taking a look at the What is Machine Learning? section to get started. If you are already familiar with basic ML concepts, you can browse the basic introduction for a brief look at a typical Rubix ML project. From there, you can browse the official tutorials below which range from beginner to advanced skill level.
Check out these example projects using the Rubix ML library. Many come with instructions and a pre-cleaned dataset.
- CIFAR-10 Image Recognizer
- Color Clusterer
- Credit Default Risk Predictor
- Divorce Predictor
- DNA Taxonomer
- Dota 2 Game Outcome Predictor
- Human Activity Recognizer
- Housing Price Predictor
- Iris Flower Classifier
- MNIST Handwritten Digit Recognizer
- Text Sentiment Analyzer
Rubix ML is funded by donations from the community. You can support this project by making a contribution to one of the funding platforms listed below.
See CONTRIBUTING.md for guidelines.
The code is licensed MIT and the documentation is licensed CC BY-NC 4.0.