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
            • 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
        • Classification of Logics
        • Formal Logic
          • Propositional Logic
            • Basics of Propositional Logic
            • Implementation in PHP
          • Predicate Logic
            • Basics of Predicate Logic
            • Implementation in PHP
          • Second-order and Higher-order Logic
          • Modal Logic
          • Temporal Logic
        • Informal Logic
        • Semi-formal Logic
      • 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
      • Linear vs Non-Linear Problems in NN
      • 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
      • NLP and Text
      • 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
  • Overview of CV
    • History of CV
    • Common Use Cases
  • CV Capabilities in PHP
  • Mathematics for CV
  • CV Techniques
  • Integrating CV into Web
  • Key Applications of CV
  • Practice
  • Robotics
    • Introduction
  • Overview of Robotics
    • History and Evolution of Robotics
    • Core Components
      • Sensors (Perception)
      • Actuators (Action)
      • Controllers (Processing and Logic)
    • The Role of AI in Robotics
      • Object Detection and Recognition
      • Path Planning and Navigation
      • Decision Making and Learning
  • Robotics Capabilities in PHP
  • Mathematics for Robotics
  • Building Robotics
  • Integration Robotics into Web
  • Key Applications of Robotics
  • Practice
  • 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
  • Supervised Learning
  • Unsupervised Learning
  • Semi-Supervised Learning
  • Self-Supervised Learning
  • Reinforcement Learning
  • Distributed Learning
  1. Machine Learning
  2. ML Algorithms
  3. Classification of ML Algorithms

By Learning Types

Let's explore deeper the meaning behind these categories and what they encompass.

Supervised Learning

Supervised learning represents a category of ML algorithms that learn from labeled training data to predict outcomes for unseen data. The algorithm learns a mapping function from input variables to output variables based on example input-output pairs.

Imagine teaching children to recognize animals: you show a picture of a dog, say “dog,” and eventually, they start spotting dogs on their own. Supervised learning works similarly by feeding an algorithm labeled data (like examples of dogs and cats) to help it learn to identify patterns and make predictions.

In simple words Supervised Learning is about teaching machines to see patterns.

Key Characteristics:

  • Requires labeled training data

  • Has defined input and output variables

  • Enables performance measurement through prediction accuracy

  • Suitable for regression and classification tasks

Supervised Learning Examples

Supervised learning is effective for a variety of business purposes, including sales forecasting, inventory optimization, medical diagnosis and fraud detection. Some examples of use cases include:

  • Medical Diagnosis: trains on patient data like symptoms and test results to help predict diseases. Used for early cancer detection, predicting diabetes risk.

  • Financial Forecasting: analyzes historical data to anticipate stock trends or credit risk. Used for detecting fraud, stock market predictions.

  • Natural Language Processing: understands text to sort emails, answer customer queries, and more. Helps for spam filters, sentiment analysis in customer reviews.

  • Real Estate: predicts real estate prices.

  • Maintenance of mechanisms: predicts the failure of industrial equipment's mechanical parts

Unsupervised Learning

Unsupervised learning algorithms work with unlabeled data to discover hidden patterns or intrinsic structures. These algorithms identify commonalities in data and respond based on the presence or absence of such commonalities in each new piece of data.

Unsupervised learning is like discovering patterns in a puzzle without the box. There are no labels; the algorithm learns to spot structures or patterns in the data all on its own.

In simple words Unsupervised Learning is about finding hidden patterns

Key Characteristics:

  • Works with unlabeled data

  • Focuses on pattern discovery

  • No predefined output variables

  • Useful for exploratory data analysis

Unsupervised learning examples

Unsupervised algorithms are widely used to create predictive models. Common applications also include clustering, which creates a model that groups objects based on certain properties, and association, which defines rules between clusters. Some examples of use cases include:

  • Market Segmentation: groups customers based on shopping habits, creating custom marketing plans. Used for targeted ads, personalized product recommendations.

  • Anomaly Detection: identifies unusual patterns that signal something different or wrong. Used for fraud detection, spotting equipment malfunctions.

  • Recommendation Systems: matches you with items based on similarities, like suggesting a new series on Netflix. Used for Netflix recommendations, Amazon product suggestions.

Semi-Supervised Learning

Semi-supervised learning combines elements of both supervised and unsupervised learning, utilizing a small amount of labeled data with a larger amount of unlabeled data. This approach is particularly valuable when obtaining labeled data is expensive or time-consuming.

Semi-supervised learning combines a small amount of labeled data with lots of unlabeled data. It’s like teaching with a few examples and letting the machine fill in the blanks — a cost-effective way to build robust models without tons of labeling.

In simple words Semi-Supervised Learning takes the best of both worlds: supervised and unsupervised learning.

Key Characteristics:

  • Combines labeled and unlabeled data

  • Reduces the need for extensive labeling

  • Often more accurate than unsupervised learning

  • Cost-effective solution for large datasets

Semi-supervised learning examples

Practical applications for this type of machine learning are still emerging. Some use cases include:

  • Speech Analysis: learns from both transcribed (labeled) and untranscribed (unlabeled) audio to improve speech systems. Used for Google Assistant, Alexa.

  • Image Classification: identifies objects in images, even with limited labeled data. Used for face recognition, content moderation.

  • Text Classification: sorts documents with minimal labeling, using context from surrounding data. Used for news categorization, tagging social media posts.

Self-Supervised Learning

Self-supervised learning is a type of machine learning where the model learns to label the data by itself, creating its own supervision. It generates labels from the input data without human intervention, making it a form of unsupervised learning with a clever twist.

Self-supervised learning trains a model by solving pretext tasks — simple problems based on the data’s structure — and then uses the learned patterns for other tasks. It’s like solving puzzles using pieces of the data itself to teach the model how to understand it better.

In simple words, Self-Supervised Learning is like teaching a machine to learn from data without needing human-labeled answers — the machine labels its own data.

Key Characteristics:

  • Learns from unlabeled data by creating its own labels

  • Reduces reliance on human-annotated data

  • Builds representations that transfer well to other tasks

  • Often used when labeling is expensive or impossible

Self-Supervised Learning Examples

Self-supervised learning powers some of the most advanced AI systems today. Real-world applications include:

  • Natural Language Processing (NLP):

    Language Models: Trains on massive text data by predicting missing words (e.g., GPT, BERT). Used for chatbots, search engines, and text generation.

  • Computer Vision: Image Understanding: Learns from images by predicting missing parts, rotating images, or identifying similar patches. Used for image search, medical imaging, and self-driving cars.

  • Audio Processing: Speech Models: Learns patterns from raw audio by predicting future sounds or missing segments. Used for virtual assistants and voice recognition.

Self-supervised learning is transforming AI by allowing models to learn from vast amounts of unlabeled data — making AI smarter, faster, and less reliant on human input.

Reinforcement Learning

Reinforcement learning involves algorithms that learn optimal actions through trial and error interactions with an environment. The algorithm receives feedback in the form of rewards or penalties and adjusts its strategy accordingly.

Imagine training a dog with treats and feedback. Reinforcement learning works similarly, using rewards and penalties to encourage machines to make better decisions over time—perfect for complex, sequential decisions.

In simple words Reinforcement Learning is about learning by rewards.

Key Characteristics:

  • Interactive learning process

  • Reward-based feedback

  • No direct supervision required

  • Emphasis on long-term strategy

Reinforcement learning examples

Practical applications for this type of machine learning are still emerging. Some use cases include:

  • Autonomous Systems: trains robots and self-driving cars to navigate safely and efficiently. Used for Tesla Autopilot, warehouse automation.

  • Game Strategy: learns optimal moves for strategy games. Used for AlphaGo, chess engines.

  • Resource Management: optimizes systems by managing resources like electricity or data. Used for power grid balancing, data center cooling.

Distributed Learning

Distributed learning encompasses algorithms designed to operate across multiple computational nodes or devices. This approach enables processing of large-scale datasets and collaborative learning while maintaining data privacy.

Imagine dividing a massive task among multiple friends to finish faster. Distributed learning splits tasks across multiple devices or servers, making large-scale learning faster and often more private, especially helpful for organizations sharing data securely.

In simple words Distributed Learning is about the power of many.

Key Characteristics:

  • Parallel processing capability

  • Scalable architecture

  • Privacy preservation options

  • Reduced central processing requirements

Distributed learning examples

Practical applications for this type of machine learning are still emerging. Some use cases include:

  • Mobile Device Learning: improves user experience without sharing personal data with central servers. Used for predictive text, virtual assistants.

  • Healthcare Analytics: analyzes patient data across hospitals for better insights without sharing private information. Used for pandemic tracking, research studies.

  • Smart Cities: uses sensors and devices throughout cities to optimize resources. Used for traffic management, energy distribution.

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