Artificial Intelligence with PHP
  • Getting Started
    • Introduction
    • Audience
    • How to Read This Book
    • Glossary
    • Contributors
    • Resources
    • Changelog
  • Artificial Intelligence
    • Introduction
    • Overview of AI
      • History of AI
      • How Does AI Work?
      • Structure of AI
      • Will AI Take Over the World?
      • Types of AI
        • Limited Memory AI
        • Reactive AI
        • Theory of Mind AI
        • Self-Aware AI
    • AI Capabilities in PHP
      • Introduction to LLM Agents PHP SDK
      • Overview of AI Libraries in PHP
    • AI Agents
      • Introduction to AI Agents
      • Structure of AI Agent
      • Components of AI Agents
      • Types of AI Agents
      • AI Agent Architecture
      • AI Agent Environment
      • Application of Agents in AI
      • Challenges in AI Agent Development
      • Future of AI Agents
      • Turing Test in AI
      • LLM AI Agents
        • Introduction to LLM AI Agents
        • Implementation in PHP
          • Sales Analyst Agent
          • Site Status Checker Agent
    • Theoretical Foundations of AI
      • Introduction to Theoretical Foundations of AI
      • Problem Solving in AI
        • Introduction
        • Types of Search Algorithms
          • Comparison of Search Algorithms
          • Informed (Heuristic) Search
            • Global Search
              • Beam Search
              • Greedy Search
              • Iterative Deepening A* Search
              • A* Search
                • A* Graph Search
                • A* Graph vs A* Tree Search
                • A* Tree Search
            • Local Search
              • Hill Climbing Algorithm
                • Introduction
                • Best Practices and Optimization
                • Practical Applications
                • Implementation in PHP
              • Simulated Annealing Search
              • Local Beam Search
              • Genetic Algorithms
              • Tabu Search
          • Uninformed (Blind) Search
            • Global Search
              • Bidirectional Search (BDS)
              • Breadth-First Search (BFS)
              • Depth-First Search (DFS)
              • Iterative Deepening Depth-First Search (IDDFS)
              • Uniform Cost Search (UCS)
            • Local Search
              • Depth-Limited Search (DLS)
              • Random Walk Search (RWS)
          • Adversarial Search
          • Means-Ends Analysis
      • Knowledge & Uncertainty in AI
        • Knowledge-Based Agents
        • Knowledge Representation
          • Introduction
          • Approaches to KR in AI
          • The KR Cycle in AI
          • Types of Knowledge in AI
          • KR Techniques
            • Logical Representation
              • Propositional Logic
              • Predicate Logic
              • Implementation in PHP
            • Semantic Network Representation
            • Frame Representation
            • Production Rules
        • Reasoning in AI
        • Uncertain Knowledge Representation
        • The Wumpus World
        • Applications and Challenges
      • Cybernetics and AI
      • Philosophical and Ethical Foundations of AI
    • Mathematics for AI
      • Computational Theory in AI
      • Logic and Reasoning
      • Set Theory and Discrete Mathematics
      • Decision Making in AI
    • Key Application of AI
      • AI in Astronomy
      • AI in Agriculture
      • AI in Automotive Industry
      • AI in Data Security
      • AI in Dating
      • AI in E-commerce
      • AI in Education
      • AI in Entertainment
      • AI in Finance
      • AI in Gaming
      • AI in Healthcare
      • AI in Robotics
      • AI in Social Media
      • AI in Software Development
      • AI in Adult Entertainment
      • AI in Criminal Justice
      • AI in Criminal World
      • AI in Military Domain
      • AI in Terrorist Activities
      • AI in Transforming Our World
      • AI in Travel and Transport
    • Practice
  • Machine Learning
    • Introduction
    • Overview of ML
      • History of ML
        • Origins and Early Concepts
        • 19th Century
        • 20th Century
        • 21st Century
        • Coming Years
      • Key Terms and Principles
      • Machine Learning Life Cycle
      • Problems and Challenges
    • ML Capabilities in PHP
      • Overview of ML Libraries in PHP
      • Configuring an Environment for PHP
        • Direct Installation
        • Using Docker
        • Additional Notes
      • Introduction to PHP-ML
      • Introduction to Rubix ML
    • Mathematics for ML
      • Linear Algebra
        • Scalars
          • Definition and Operations
          • Scalars with PHP
        • Vectors
          • Definition and Operations
          • Vectors in Machine Learning
          • Vectors with PHP
        • Matrices
          • Definition and Types
          • Matrix Operations
          • Determinant of a Matrix
          • Inverse Matrices
          • Cofactor Matrices
          • Adjugate Matrices
          • Matrices in Machine Learning
          • Matrices with PHP
        • Tensors
          • Definition of Tensors
          • Tensor Properties
            • Tensor Types
            • Tensor Dimension
            • Tensor Rank
            • Tensor Shape
          • Tensor Operations
          • Practical Applications
          • Tensors in Machine Learning
          • Tensors with PHP
        • Linear Transformations
          • Introduction
          • LT with PHP
          • LT Role in Neural Networks
        • Eigenvalues and Eigenvectors
        • Norms and Distances
        • Linear Algebra in Optimization
      • Calculus
      • Probability and Statistics
      • Information Theory
      • Optimization Techniques
      • Graph Theory and Networks
      • Discrete Mathematics and Combinatorics
      • Advanced Topics
    • Data Fundamentals
      • Data Types and Formats
        • Data Types
        • Structured Data Formats
        • Unstructured Data Formats
        • Implementation with PHP
      • General Data Processing
        • Introduction
        • Storage and Management
          • Data Security and Privacy
          • Data Serialization and Deserialization in PHP
          • Data Versioning and Management
          • Database Systems for AI
          • Efficient Data Storage Techniques
          • Optimizing Data Retrieval for AI Algorithms
          • Big Data Considerations
            • Introduction
            • Big Data Techniques in PHP
      • ML Data Processing
        • Introduction
        • Types of Data in ML
        • Stages of Data Processing
          • Data Acquisition
            • Data Collection
            • Ethical Considerations in Data Preparation
          • Data Cleaning
            • Data Cleaning Examples
            • Data Cleaning Types
            • Implementation with PHP
          • Data Transformation
            • Data Transformation Examples
            • Data Transformation Types
            • Implementation with PHP ?..
          • Data Integration
          • Data Reduction
          • Data Validation and Testing
            • Data Splitting and Sampling
          • Data Representation
            • Data Structures in PHP
            • Data Visualization Techniques
          • Typical Problems with Data
    • ML Algorithms
      • Classification of ML Algorithms
        • By Methods Used
        • By Learning Types
        • By Tasks Resolved
        • By Feature Types
        • By Model Depth
      • Supervised Learning
        • Regression
          • Linear Regression
            • Types of Linear Regression
            • Finding Best Fit Line
            • Gradient Descent
            • Assumptions of Linear Regression
            • Evaluation Metrics for Linear Regression
            • How It Works by Math
            • Implementation in PHP
              • Multiple Linear Regression
              • Simple Linear Regression
          • Polynomial Regression
            • Introduction
            • Implementation in PHP
          • Support Vector Regression
        • Classification
        • Recommendation Systems
          • Matrix Factorization
          • User-Based Collaborative Filtering
      • Unsupervised Learning
        • Clustering
        • Dimension Reduction
        • Search and Optimization
        • Recommendation Systems
          • Item-Based Collaborative Filtering
          • Popularity-Based Recommendations
      • Semi-Supervised Learning
        • Regression
        • Classification
        • Clustering
      • Reinforcement Learning
      • Distributed Learning
    • Integrating ML into Web
      • Open-Source Projects
      • Introduction to EasyAI-PHP
    • Key Applications of ML
    • Practice
  • Neural Networks
    • Introduction
    • Overview of NN
      • History of NN
      • Basic Components of NN
        • Activation Functions
        • Connections and Weights
        • Inputs
        • Layers
        • Neurons
      • Problems and Challenges
      • How NN Works
    • NN Capabilities in PHP
    • Mathematics for NN
    • Types of NN
      • Classification of NN Types
      • Basic NN
        • Simple Perceptron
        • Implementation in PHP
          • Simple Perceptron with Libraries
          • Simple Perceptron with Pure PHP
      • NN with Hidden Layers
      • Deep Learning
      • Bayesian Neural Networks
      • Convolutional Neural Networks (CNN)
      • Recurrent Neural Networks (RNN)
    • Integrating NN into Web
    • Key Applications of NN
    • Practice
  • Natural Language Processing
    • Introduction
    • Overview of NLP
      • History of NLP
        • Ancient Times
        • Medieval Period
        • 15th-16th Century
        • 17th-18th Century
        • 19th Century
        • 20th Century
        • 21st Century
        • Coming Years
      • Key Concepts in NLP
      • Common Challenges in NLP
      • Machine Learning Role in NLP
    • NLP Capabilities in PHP
      • Overview of NLP Libraries in PHP
      • Challenges in NLP with PHP
    • Mathematics for NLP
    • NLP Techniques
      • Basic Text Processing with PHP
      • NLP Workflow
      • Popular Tools and Frameworks for NLP
      • Techniques and Algorithms in NLP
        • Basic NLP Techniques
        • Advanced NLP Techniques
      • Advanced NLP Topics
    • Integrating NLP into Web
    • Key Applications of NLP
    • Practice
  • Computer Vision
    • Introduction
  • Robotics
    • Introduction
  • Expert Systems
    • Introduction
    • Overview of ES
      • History of ES
        • Origins and Early ES
        • Milestones in the Evolution of ES
        • Expert Systems in Modern AI
      • Core Components and Architecture
      • Challenges and Limitations
      • Future Trends
    • ES Capabilities in PHP
    • Mathematics for ES
    • Building ES
      • Knowledge Representation Approaches
      • Inference Mechanisms
      • Best Practices for Knowledge Base Design and Inference
    • Integration ES into Web
    • Key Applications of ES
    • Practice
  • Cognitive Computing
    • Introduction
    • Overview of CC
      • History of CC
      • Differences Between CC and AI
    • CC Compatibilities in PHP
    • Mathematics for CC
    • Building CC
      • Practical Implementation
    • Integration CC into Web
    • Key Applications of CC
    • Practice
  • AI Ethics and Safety
    • Introduction
    • Overview of AI Ethics
      • Core Principles of AI Ethics
      • Responsible AI Development
      • Looking Ahead: Ethical AI Governance
    • Building Ethics & Safety AI
      • Fairness, Bias, and Transparency
        • Bias in AI Models
        • Model Transparency and Explainability
        • Auditing, Testing, and Continuous Monitoring
      • Privacy and Security in AI
        • Data Privacy and Consent
        • Safety Mechanisms in AI Integration
        • Preventing and Handling AI Misuse
      • Ensuring AI Accountability
        • Ethical AI in Decision Making
        • Regulations & Compliance
        • AI Risk Assessment
    • Key Applications of AI Ethics
    • Practice
  • Epilog
    • Summing-up
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On this page
  • Introduction
  • Why Use PHP for Machine Learning?
  • Popular Machine Learning Libraries in PHP
  • PHP Mathematical Libraries (for scientific computing)
  • PHP Extensions
  • JavaScript with PHP Integration
  • Benefits of Machine Learning in PHP
  • Challenges and Limitations
  • Conclusion
  1. Machine Learning
  2. ML Capabilities in PHP

Overview of ML Libraries in PHP

PreviousML Capabilities in PHPNextConfiguring an Environment for PHP

Last updated 17 days ago

Introduction

Machine learning has become an essential tool in modern software development, helping to build intelligent systems that can learn from data and make predictions. While languages like Python and R are typically associated with machine learning, PHP, a popular server-side scripting language, has also embraced machine learning capabilities. In recent years, several machine learning libraries have emerged in PHP, allowing developers to implement machine learning solutions without switching to a different programming language.

Why Use PHP for Machine Learning?

PHP is widely used for web development and powers a significant portion of the internet. Although PHP wasn't originally designed for machine learning, the availability of ML libraries in PHP makes it easier for web developers to integrate intelligent features, such as recommendation engines, predictive analytics, or data classification, directly into their web applications.

Popular Machine Learning Libraries in PHP

1. Rubix ML

Rubix ML is another powerful PHP library that is designed to be easy to use while still offering advanced machine learning capabilities. It supports a wide variety of supervised and unsupervised learning algorithms, including neural networks, decision trees, and clustering methods. Rubix ML is known for its flexibility and performance, making it suitable for production-level applications. It also provides tools for data transformation, pipelines, and model evaluation.

2. PHP-ML (PHP Machine Learning Library)

PHP-ML is one of the most comprehensive machine learning libraries for PHP. It provides a wide range of algorithms for classification, regression, clustering, and more. Some of the key features include:

  • Algorithms such as Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Naive Bayes.

  • Data pre-processing tools like normalization and feature extraction.

  • Cross-validation methods for evaluating model performance. This library is perfect for developers who need to add basic machine learning functionalities to their PHP applications without a deep understanding of ML.

PHP Mathematical Libraries (for scientific computing)

1. NumPower

NumPower is a PHP extension designed for high-performance numerical computing (currently only for x86-64 (AVX2 instructions)), offering efficient multi-dimensional array operations and linear algebra functions, similar to Python's NumPy. It provides GPU acceleration through CUDA support, enabling parallel computations on NVIDIA GPUs, significantly enhancing performance for large-scale data processing and scientific computing tasks.

2. MathPHP

Powerful Modern Math Library for PHP. The only library you need to integrate mathematical functions into your applications. It is a self-contained library in pure PHP with no external dependencies.

3. RubixML/Tensor

The RubixML/Tensor library is a high-performance linear algebra extension for PHP, designed for efficient numerical computing. It provides fast, memory-optimized tensor and matrix operations, including element-wise arithmetic, decompositions, and transformations. Built with PHP’s native extensions and optimized for speed, Tensor enables developers to perform complex mathematical computations essential for machine learning and data science applications without relying on external dependencies like NumPy or BLAS.

4. NumPHP

NumPHP is a PHP library for scientific computing, inspired by NumPy in Python. It provides multi-dimensional arrays and mathematical operations optimized for performance, making it easier to work with numerical data in PHP. With support for matrix operations, linear algebra, and statistical functions, NumPHP is particularly useful for developers working on data analysis, machine learning, and numerical computations in a PHP environment.

5. SciPhp

SciPhp is a PHP library designed for scientific computing, providing a NumPy-like experience in PHP. It offers multi-dimensional arrays (ndarrays), along with mathematical, statistical, and linear algebra functions, making it easier to perform numerical computations within PHP applications.

PHP Extensions

1. LibSVM (PHP Extension)

A PHP wrapper for the LibSVM library, which provides support for Support Vector Machines (SVM). This extension allows PHP developers to use SVM for classification and regression tasks.

JavaScript with PHP Integration

1. TensorFlow.js (for JavaScript with PHP Integration)

TensorFlow is one of the most popular machine learning libraries globally, but it is typically used with Python. However, there are ways to integrate TensorFlow models into PHP applications through APIs and bridges. For example, developers can use TensorFlow.js to run models in JavaScript on the client side and communicate with PHP for data processing. Although PHP does not natively support TensorFlow, using such workarounds makes it possible to leverage TensorFlow’s capabilities.

2. Brain.js (for JavaScript with PHP Integration)

While Brain.js is a JavaScript-based neural network library, it can be combined with PHP to enable machine learning on the client side. It’s useful for handling simple machine learning tasks directly in the browser, while PHP handles server-side operations.

Benefits of Machine Learning in PHP

  • Seamless Web Integration: Since PHP is predominantly used in web development, using PHP for machine learning allows for the direct integration of intelligent features into web applications without the need to switch languages.

  • Familiarity for Developers: PHP developers can implement machine learning solutions without learning new programming languages, enabling them to build more sophisticated applications while staying within their comfort zone.

  • Server-Side Capabilities: With PHP, you can handle machine learning operations on the server, offering more control over the data and results.

Challenges and Limitations

While PHP has some useful machine learning libraries, it is still not as mature in this domain as Python or R. Many advanced tools and libraries available in Python, such as deep learning frameworks like TensorFlow and PyTorch, are more challenging to implement in PHP. Therefore, for more complex machine learning tasks, developers may still need to rely on other languages.

Conclusion

Machine learning in PHP is no longer just a theoretical possibility. Libraries like Rubix ML and PHP-ML allow developers to incorporate intelligent features into their applications without leaving the PHP ecosystem. Although PHP may not be the first choice for machine learning, it offers a practical option for web developers who want to bring machine learning capabilities to their projects without switching languages.

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Official website: (hasn't been updated for a long time)

Official website: (hasn't been updated for a long time)

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Official website:

https://rubixml.com
https://php-ml.readthedocs.io
https://numpower.org
https://github.com/markrogoyski/math-php
https://github.com/RubixML/Tensor
https://numphp.org
https://sciphp.org
https://github.com/ianbarber/php-svm
https://www.tensorflow.org
https://brain.js.org
Overview of Machine Learning Libraries in PHP