A high-level machine learning and deep learning library for the PHP language.
-
Updated
May 10, 2025 - PHP
A high-level machine learning and deep learning library for the PHP language.
An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.
Simple stock & cryptocurrency price forecasting console application, using PHP Machine Learning library (https://github.com/php-ai/php-ml)
naive bayes in php
A standalone inference server for trained Rubix ML estimators.
Use the famous CIFAR-10 dataset to train a multi-layer neural network to recognize images of cats, dogs, and other things.
An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.
Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.
The original lightweight introduction to machine learning in Rubix ML using the famous Iris dataset and the K Nearest Neighbors classifier.
An example project that predicts risk of credit card default using a Logistic Regression classifier and a 30,000 sample dataset.
Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.
Demonstrating unsupervised clustering using the K Means algorithm and synthetic color data.
Use the K Nearest Neighbors algorithm to predict the probability of a divorce with high accuracy.
Build a classifier to predict the outcome of Dota 2 games with the Naive Bayes algorithm and results from 102,944 sample games.
Sistem Pendukung Keputusan untuk menentukan layak dan tidaknya seseorang mendapatkan bantuan PKH dengan menggunakan machine learning yaitu C4.5 dan K-Means
HelloAI - A cloud based artificial intelligence platform
Example Machine Learning with MySQL via PHP with Apache2
Add a description, image, and links to the php-ml topic page so that developers can more easily learn about it.
To associate your repository with the php-ml topic, visit your repo's landing page and select "manage topics."