dota2's Introduction

Rubix ML

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

Installation

Install Rubix ML into your project using Composer:

$ composer require rubix/ml

Requirements

PHP 7.4 or above

Optional

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
GraphViz for graph visualization

Documentation

Read the latest docs here.

What is Rubix ML?

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.

Getting Started

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.

Tutorials & Example Projects

Check out these example projects using the Rubix ML library. Many come with instructions and a pre-cleaned dataset.

Interact With The Community

Join Our Telegram Channel

Contributing

See CONTRIBUTING.md for guidelines.

License

The code is licensed MIT and the documentation is licensed CC BY-NC 4.0.

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dota2's Issues

Create the tutorial!

Are you not so naive when it comes to Naive Bayes? We need you to take your knowledge and intuition and lay it down in a tutorial for an intermediate user to digest. Want to get fancy? Maybe mention something about the strong feature independence assumption and how that kinda but doesn't really matter too much IRL. Feeling creative? Include some charts! Oh and don't forget to include Bayes' Theorem, that's kinda important too.

Since we have both a training and validation script, we'll need to cover extraction, training, persisting, and cross-validation. You can use our other tutorials as a guide for structure and depth.

Recommend Projects

rubixml / dota2 Goto Github PK