This is a PHP-ML Chinese learning example

An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.

An example project that predicts risk of credit card default using a Logistic Regression classifier and a 30,000 sample dataset.

An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.

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.

Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.

Build a classifier to predict the outcome of Dota 2 games with the Naive Bayes algorithm and results from 102,944 sample games.

Demonstrating unsupervised clustering using the K Means algorithm and synthetic color data.

Use the famous CIFAR-10 dataset to train a multi-layer neural network to recognize images of cats, dogs, and other things.

A standalone inference server for trained Rubix ML estimators.

analisador de sentimentos desenvolvido em PHP, que utiliza a biblioteca PHP-ML para construir e treinar modelos de classificação (neste caso, utilizando o algoritmo Naive Bayes) para identificar o sentimento de textos – por exemplo, se um tweet é positivo, negativo ou neutro.

Simple stock & cryptocurrency price forecasting console application, using PHP Machine Learning library (https://github.com/php-ai/php-ml)

This project is an ordering application designed for cafes. It allows customers to place orders, make reservations, and process payments online. The application also provides cafe management with sales analytics and forecasting tools to better understand and predict their business performance.

A machine learning based chatbot library for PHP.

Sistem Pendukung Keputusan untuk menentukan layak dan tidaknya seseorang mendapatkan bantuan PKH dengan menggunakan machine learning yaitu C4.5 dan K-Means

Enhancing dining experiences with advanced database techniques, PHP-ML for data mining, and Chart.js for insights.

Use the K Nearest Neighbors algorithm to predict the probability of a divorce with high accuracy.

naive bayes in php

Machine Learning library for PHP