A curated, ever-growing roadmap for learning Machine Learning, Deep Learning, and AI. This comprehensive collection spans foundational mathematics, courses, demos, books, frameworks, open source LLMs, and EdTech tools—each resource is free or open-access for maximum accessibility.
This library is organized from foundational concepts to advanced applications. Start with 🔢 Mathematics Foundations if you're new to ML, or jump directly to specific areas based on your needs.
- 🔢 Mathematics Foundations
- 📊 Linear Algebra
- 📈 Calculus & Optimization
- 🎲 Probability & Statistics
- 🤖 Machine Learning
- 🧠 Deep Learning
- 🎮 Reinforcement Learning
- 💻 Coding Resources
- 🔧 Frameworks/Libraries
- 📚 Books/Blogs
- 🎯 Interactive Demos
- 🗣️ LLMs & Generative AI
- 🎓 EdTech & Course Creation Tools
- 🏭 Miscellaneous/Industry
Build the mathematical foundation essential for machine learning success. Mathematics forms the bedrock of AI—from linear algebra powering neural networks to statistics enabling data analysis and calculus driving optimization algorithms. Master these core concepts through proven textbooks, interactive courses, and practical resources designed to bridge theory with real-world ML applications.
| Resource | Type | Description |
|---|---|---|
| Mathematics for Machine Learning | 📖 Book | Comprehensive mathematical foundations for ML |
| Khan Academy Mathematics | 🎓 Course | Interactive math courses from basics to advanced |
| 3Blue1Brown Mathematics Playlist | 🎥 Video | Visual mathematics explanations for intuitive understanding |
| MIT Mathematics for Computer Science | 🎓 Course | Discrete mathematics and probability for CS |
| Elements of Statistical Learning | 📖 Book | Classic statistical learning theory |
| Introduction to Statistical Learning | 📖 Book | Beginner-friendly statistical learning |
| Pattern Recognition and Machine Learning | 📖 Book | Bishop's comprehensive ML textbook |
| Probability and Statistics for Engineers | 📚 Reference | Complete probability and statistics resource |
| Mathematical Thinking in Computer Science | 🎓 Course | Coursera course on mathematical reasoning |
| Math Prerequisites for ML CheatSheet | 📋 Reference | Stanford's quick math reference for ML |
| Ace the Data Science Interview | 📖 Book | Interview prep with mathematical focus |
Vectors, matrices, and transformations form the mathematical backbone of machine learning. Linear algebra concepts like eigenvalues, matrix decomposition, and vector spaces are essential for understanding neural networks, dimensionality reduction, and optimization algorithms that power modern AI systems.
- Linear Algebra by 3Blue1Brown 🎥 – Visual and intuitive approach to linear algebra
- Khan Academy Linear Algebra 🎓 – Step-by-step tutorials with practice problems
- MIT 18.06 Linear Algebra by Gilbert Strang 🎥 – Complete MIT course lectures
- Linear Algebra by Professor Leonard 🎥 – Comprehensive lecture series
- Immersive Linear Algebra 💻 – Interactive web book with visualizations
- Linear Algebra Explained Visually 🎨 – Interactive eigenvalue/eigenvector visualization
- Introduction to Linear Algebra by Gilbert Strang 📖 – MIT's foundational textbook with clear explanations
- Linear Algebra Done Right by Sheldon Axler 📖 – Conceptual approach focusing on understanding over computation
- Matrix Cookbook 📖 – Quick reference for matrix identities and formulas
- Linear Algebra for ML CheatSheet 📋 – Quick reference for ML applications
- Principal Component Analysis Explained 🎥 – PCA for dimensionality reduction
- Singular Value Decomposition Explained 🎥 – Deep dive into SVD
- Matrix Factorization Techniques 🎥 – Essential for recommender systems
- NumPy Linear Algebra Tutorial 💻 – Implementing linear algebra in Python
Derivatives, gradients, and optimization algorithms drive the learning process in machine learning models. Understanding calculus concepts like chain rule, partial derivatives, and gradient descent is fundamental to neural network training, parameter optimization, and the mathematical principles behind how AI systems learn and improve.
- Essence of Calculus by 3Blue1Brown 🎥 – Visual calculus fundamentals
- MIT Single Variable Calculus 🎓 – MIT's comprehensive calculus course
- Khan Academy Calculus 🎥 – Interactive single variable calculus lessons
- MIT Multivariable Calculus 🎓 – Essential for understanding gradients and partial derivatives
- Khan Academy Multivariable Calculus 🎥 – Interactive lessons on partial derivatives and optimization
- Convex Optimization by Boyd & Vandenberghe 📖 – Stanford's foundational optimization textbook (free PDF)
- Algorithms for Optimization 📖 – Modern optimization techniques
- Introduction to Optimization Theory 🎓 – MIT nonlinear programming course
- Numerical Optimization by Nocedal & Wright 📖 – Comprehensive optimization algorithms
- Backpropagation Calculus 🎥 – 3Blue1Brown's neural network calculus
- Gradient Descent Explained 🎥 – Visual explanation of gradient descent
- Stochastic Optimization Methods 🎥 – SGD, Adam, and modern optimizers
- Optimization for Machine Learning 📋 – Practical optimization tricks for ML
- Lagrange Multipliers Explained 🎥 – Constrained optimization fundamentals
- Gradient Descent Playground 🎮 – Visualize optimization in neural networks
- Calculus Visualization Tool 💻 – Interactive calculus exploration
- Numerical Methods Primer 📚 – Practical numerical computation
- SciPy Optimization Tutorial 💻 – Python optimization tools
- Autograd Tutorial 💻 – Automatic differentiation in Python
- JAX Optimization Examples 💻 – Modern optimization with JAX
Statistical thinking and probability theory form the foundation of data science and machine learning. From understanding data distributions and hypothesis testing to Bayesian inference and uncertainty quantification, these concepts enable you to make sense of data, build robust models, and interpret AI system outputs with confidence.
- Khan Academy Statistics 🎓 – Interactive statistics and probability fundamentals
- Seeing Theory (Interactive) 🎯 – Interactive probability visualizations
- StatQuest with Josh Starmer 🎥 – Clear explanations of statistical concepts
- MIT Introduction to Probability 🎓 – Rigorous probability theory
- Introduction to Probability by Blitzstein & Hwang 📖 – Harvard's probability textbook (free online)
- Statistics 110: Probability 🎥 – Harvard's probability course lectures
- Probability Theory for Data Science 📖 – Applied probability for data science
- Think Stats 2e 📖 – Probability and statistics for programmers
- OpenIntro Statistics 📖 – Free introductory statistics textbook
- Think Bayes 📖 – Bayesian statistics with Python examples
- Bayesian Methods for Hackers 📚 – Practical Bayesian analysis
- Statistical Rethinking 📖 – Modern Bayesian data analysis
- Scipy Statistics Tutorial 💻 – Statistical functions in Python
- Statsmodels Documentation 💻 – Statistical modeling in Python
- R for Data Science - Statistics 📚 – Statistical analysis with R
Master the algorithms and techniques that power intelligent systems. Machine learning transforms data into insights through supervised learning, unsupervised learning, and reinforcement learning approaches. From regression and classification to clustering and neural networks, these resources guide you through both theoretical understanding and hands-on implementation of ML systems.
- Machine Learning Course by Andrew Ng – Stanford's foundational ML course
- Machine Learning Crash Course by Google – Google's practical ML intro
- Fast.ai Practical Machine Learning – Top-down practical approach
- MIT Introduction to Machine Learning – MIT's comprehensive course
- Kaggle Learn – Micro-courses with hands-on practice
- AI For Everyone – Andrew Ng – Non-technical AI overview
- Machine Learning Mastery – Practical tutorials and guides
- The Elements of Statistical Learning – Advanced statistical learning theory
- Foundations of Machine Learning – Mohri et al. – Theoretical foundations
- Pattern Recognition and Machine Learning – Bishop – Comprehensive ML textbook
- Scikit-learn User Guide – Practical ML with Python
- Hands-On Machine Learning – Practical ML with Scikit-Learn and TensorFlow
- Approaching (Almost) Any ML Problem – Practical problem-solving guide
- CS229 Machine Learning Cheatsheets – Stanford's ML reference sheets
- Interpretable Machine Learning – Understanding ML model decisions
- Feature Engineering and Selection – Data preprocessing techniques
- 3Blue1Brown Neural Networks – Visual neural network explanations
- StatQuest Machine Learning – Clear ML concept explanations
Neural networks with multiple layers unlock artificial intelligence's most powerful capabilities. Deep learning drives breakthrough applications in computer vision, natural language processing, and generative AI. Master convolutional networks, transformers, and modern architectures that power everything from image recognition to large language models.
- Deep Learning – Goodfellow, Bengio, Courville – Definitive deep learning textbook
- Neural Networks: Zero to Hero 🎥 – Karpathy's hands-on neural networks
- 3Blue1Brown Neural Networks 🎥 – Visual neural network explanations
- DeepLearning.AI Courses – Andrew Ng's deep learning specialization
- Deep Learning for Coders 🎓 – Practical deep learning with fast.ai
- CS231n: Convolutional Neural Networks – Stanford's computer vision course
- CS224n: Natural Language Processing – Stanford's NLP with deep learning
- Attention Is All You Need – Original transformer paper
- Hugging Face NLP Course – Transformers and NLP
- The Illustrated Transformer – Visual transformer explanation
- Dive into Deep Learning – Interactive deep learning book
- PyTorch Tutorials – Official PyTorch learning resources
- TensorFlow Deep Learning – TensorFlow guides and tutorials
- Keras Documentation – High-level neural network API
- Papers with Code – Latest research with implementations
- Full Stack Deep Learning 🎓 – Production ML systems
- Made With ML – End-to-end ML development
- MLOps Specialization – Production ML workflows
- Generative Deep Learning – GANs, VAEs, and generative models
- Deep Reinforcement Learning – RL theory and applications
- Computer Vision: Algorithms and Applications – CV fundamentals and deep learning
Learn how algorithms optimize behavior through reward signals and environmental feedback. Reinforcement learning solves sequential decision problems where systems learn optimal actions through trial and error. Master the mathematical foundations of Q-learning, policy gradients, and value functions that power game-playing AI, robotics, and recommendation systems.
- RL: An Introduction – Sutton & Barto 📖 – The RL bible
- CS285: Deep Reinforcement Learning 🎓 – Berkeley's comprehensive RL course
- OpenAI Spinning Up 📚 – RL from first principles
- David Silver's RL Course 🎥 – DeepMind's RL lectures
- Practical RL Course 📚 – Hands-on RL implementations
- Stable Baselines3 🔧 – Production-ready RL algorithms
- Ray RLlib 🔧 – Scalable RL library
- Tensorforce 🔧 – RL framework
- OpenAI Gym 🎮 – Standard RL environment interface
- Unity ML-Agents 🎮 – RL in Unity environments
- CARLA Simulator 🚗 – Autonomous driving simulation
- ALE: Arcade Learning Environment 🎮 – Atari game environments
- Multi-Agent RL 🤖 – Multiple agent coordination
- Meta-Learning in RL 📄 – Learning to learn quickly
- Hierarchical RL 📄 – Multi-level decision making
- Safe RL 📄 – Constraint-aware learning
- RL Visualizations 💻 – Interactive RL concept exploration
- Q-Learning Playground 🎮 – Browser-based RL experiments
- RL Baselines Zoo 🔧 – Pre-trained RL models
- Deep RL Papers 📄 – Curated research papers
- RL in Real World 📄 – Practical RL deployment challenges
- AlphaGo Paper 📄 – Breakthrough game-playing AI
Essential development environments, programming tools, and platforms for machine learning implementation. From cloud-based notebooks and version control to specialized ML frameworks and deployment tools, these resources provide the technical foundation needed to build, train, and deploy machine learning models effectively.
| Platform | Description | Best For |
|---|---|---|
| Kaggle Notebooks | Free GPU/TPU notebooks with datasets | Competitions & learning |
| Google Colab | Free Jupyter notebooks with GPU | Prototyping & education |
| Jupyter Notebooks | Interactive computing environment | Local development |
| Deepnote | Collaborative data science notebooks | Team collaboration |
| Databricks | Unified analytics platform | Enterprise ML workflows |
- Python.org – Primary ML programming language
- Anaconda – Python distribution for data science
- PyCharm – Professional Python IDE
- VS Code – Lightweight editor with ML extensions
- R for Data Science – R programming for statistics
- Git Handbook – Version control fundamentals
- GitHub – Code hosting and collaboration
- DVC (Data Version Control) – Versioning for ML projects
- MLflow – ML lifecycle management
- GitHub Copilot – AI pair programming
- Tabnine – AI code completion
- Cursor – AI-powered code editor
- Replit – Online IDE with AI assistance
- Scikit-learn – Essential ML library for Python
- TensorFlow – Google's ML framework
- PyTorch – Facebook's deep learning framework
- Keras – High-level neural network API
- Pandas – Data manipulation and analysis
- NumPy – Numerical computing foundation
- AWS SageMaker – Amazon's ML platform
- Google Cloud AI – Google's ML services
- Azure Machine Learning – Microsoft's ML platform
- Hugging Face – NLP models and datasets
- Papers With Code – Research papers with implementations
- Weights & Biases – Experiment tracking and visualization
- Streamlit – Web app framework for ML
- Gradio – Quick ML model interfaces
- Docker – Containerization for reproducible environments
Build machine learning systems with battle-tested frameworks and specialized libraries. From foundational tools like TensorFlow and PyTorch for deep learning to domain-specific libraries for computer vision, NLP, and data processing, these resources provide the building blocks for implementing ML algorithms and deploying models in production environments.
| Library | Category | Description | Best For |
|---|---|---|---|
| NumPy | Foundation | Numerical computing fundamentals | Array operations, linear algebra |
| Pandas | Data Processing | Data manipulation and analysis | Data cleaning, exploration |
| Matplotlib | Visualization | Comprehensive plotting library | Static plots, publication figures |
| Seaborn | Visualization | Statistical data visualization | Statistical plots, attractive defaults |
| Plotly | Visualization | Interactive plotting library | Interactive dashboards, web apps |
| Framework | Developed By | Description | Strengths |
|---|---|---|---|
| TensorFlow | Comprehensive ML platform | Production deployment, mobile/edge | |
| PyTorch | Meta | Research-focused framework | Dynamic graphs, research flexibility |
| Keras | Community | High-level neural networks API | Beginner-friendly, rapid prototyping |
| JAX | NumPy-compatible ML library | Functional programming, performance |
- Scikit-learn – Comprehensive ML library with consistent API
- XGBoost – Gradient boosting framework
- LightGBM – Microsoft's gradient boosting
- CatBoost – Yandex's gradient boosting for categorical features
- Optuna – Hyperparameter optimization framework
- OpenCV – Computer vision and image processing
- Pillow (PIL) – Python imaging library
- Albumentations – Image augmentation library
- YOLO – Real-time object detection
- Detectron2 – Facebook's object detection platform
- NLTK – Natural language toolkit
- SpaCy – Industrial-strength NLP
- Hugging Face Transformers – State-of-the-art NLP models
- Gensim – Topic modeling and document similarity
- TextBlob – Simple text processing
- LangChain – LLM application development framework
- LangGraph – Graph-based LLM workflows
- Haystack – NLP framework for search and QA
- LlamaIndex – Data framework for LLM applications
- Guidance – Programming language for LLMs
- SHAP – Model explanation framework
- LIME – Local interpretable model explanations
- MLflow – ML lifecycle management
- Weights & Biases – Experiment tracking and visualization
- DVC – Data version control for ML
- RAPIDS – GPU-accelerated data science
- Dask – Parallel computing in Python
- Ray – Distributed computing framework
- Modin – Distributed pandas
- CuPy – NumPy-compatible library for GPU
- R: Caret, Tidymodels, MLR3
- JavaScript: TensorFlow.js, Brain.js
- Julia: Flux.jl, MLJ.jl
- Java: Weka, Deeplearning4j
- C++: PyTorch C++, TensorFlow C++
Deepen your understanding through carefully selected books, blogs, and educational content from leading researchers and practitioners. These resources span foundational theory to cutting-edge applications, providing comprehensive knowledge for mastering machine learning concepts, staying current with research developments, and understanding real-world implementation challenges.
| Book | Authors | Focus Area | Level |
|---|---|---|---|
| AI: A Modern Approach | Russell & Norvig | General AI Concepts | Intermediate |
| Deep Learning | Goodfellow, Bengio, Courville | Deep Learning Theory | Advanced |
| Hands-On Machine Learning | Géron | Practical Implementation | Beginner-Intermediate |
| Pattern Recognition and ML | Bishop | Statistical Learning | Advanced |
| The Elements of Statistical Learning | Hastie, Tibshirani, Friedman | Statistical Methods | Advanced |
- Python Machine Learning by Sebastian Raschka – Practical ML with Python
- Designing Data-Intensive Applications by Martin Kleppmann – Systems for ML at scale
- Building Machine Learning Powered Applications by Emmanuel Ameisen – End-to-end ML projects
- Machine Learning Design Patterns by Lakshmanan et al. – Production ML patterns
- Neural Networks and Deep Learning – Michael Nielsen's accessible deep learning introduction
- Dive into Deep Learning – Interactive deep learning with code examples
- Interpretable Machine Learning – Understanding model decisions
- The Hundred-Page Machine Learning Book – Concise ML overview by Andriy Burkov
- Jay Alammar's Blog – Visual explanations of transformers and NLP
- Distill.pub – Interactive machine learning research
- Chip Huyen's Blog – ML engineering and production insights
- Christopher Olah's Blog – Deep learning visualization and understanding
- Sebastian Ruder's Blog – NLP research and trends
- Machine Learning Engineering by Chip Huyen – Production ML systems
- Data Science for Business by Provost & Fawcett – Business applications
- Reliable Machine Learning – Production ML best practices
- ML Engineering with Python – End-to-end ML workflows
- The Gradient – AI research magazine with accessible articles
- Towards Data Science – Medium publication with practical tutorials
- Papers With Code Blog – Latest research with implementations
- AI Research – Weekly research roundups and analysis
- The Batch by deeplearning.ai – Weekly AI news newsletter
- Probabilistic Machine Learning by Kevin Murphy – Advanced probabilistic methods
- Information Theory, Inference & Learning by David MacKay – Mathematical foundations
- Convex Optimization by Boyd & Vandenberghe – Optimization theory
- Computer Vision: Models, Learning, and Inference by Simon Prince – CV fundamentals
Learn machine learning concepts through interactive visualizations and hands-on demonstrations. These tools let you experiment with algorithms, explore model architectures, and understand complex AI concepts through direct manipulation and real-time feedback, making abstract mathematical concepts tangible and intuitive.
- Neural Network Playground – Experiment with neural network architectures
- CNN Explainer – Interactive convolutional neural network visualization
- Transformer Explainer – Understanding transformer architecture visually
- Neural Network 3D Visualization – 3D neural network architecture explorer
- Clustering Playground – Compare clustering algorithms
- Decision Tree Visualization – Interactive decision tree explanation
- SVM Interactive Demo – Support Vector Machine visualization
- K-Means Clustering Demo – Interactive K-means algorithm
- Seeing Theory – Interactive probability and statistics
- Explorable Explanations – Interactive data science concepts
- Observable Notebooks – Interactive data visualization platform
- Streamlit Gallery – Interactive ML app examples
- RL Playground – Deep Q-Learning Flappy Bird demo
- OpenAI Gym Visualizations – RL environment demonstrations
- Q-Learning Maze Demo – Interactive Q-learning examples
- Policy Gradient Visualization – RL policy visualization
- Attention Visualization – Transformer attention patterns
- Word2Vec Visualization – Word embedding explorer
- BERT Visualization – BERT attention head analysis
- Language Model Playground – GPT model experimentation
- ConvNetJS Demos – Browser-based computer vision
- Image Classification Demo – Train your own image classifier
- Object Detection Demo – Real-time object detection
- Style Transfer Demo – Neural style transfer
- GAN Lab – Interactive GAN training visualization
- This Person Does Not Exist – StyleGAN face generation
- ArtBreeder – Collaborative AI art creation
- RunwayML – Creative AI tools and experiments
- Distill.pub – Interactive machine learning research articles
- Explained Visually – Visual explanations of complex concepts
- Machine Learning for Artists – Creative applications of ML
- AI Education Project – Interactive AI learning modules
Master large language models and generative AI systems that understand and generate human-like text. From transformer architectures and attention mechanisms to prompt engineering and fine-tuning techniques, explore the models and methods powering modern AI applications like ChatGPT, code generation, and creative writing assistants.
- Attention Is All You Need – Original transformer paper
- The Illustrated Transformer – Visual transformer explanation
- CS224N: NLP with Deep Learning – Stanford's comprehensive NLP course
- Hugging Face NLP Course – Practical transformer training
| Model Family | Organization | Key Features | Best For |
|---|---|---|---|
| LLaMA 4 | Meta | Efficient architecture, strong performance | Research, fine-tuning |
| Mistral 7B | Mistral AI | High performance, commercial friendly | Production applications |
| Gemma | Lightweight, responsible AI | Edge deployment | |
| Phi-3 | Microsoft | Small but capable | Resource-constrained environments |
- Hugging Face Transformers – Industry-standard library for LLMs
- Ollama – Local LLM deployment and management
- LM Studio – Desktop app for running LLMs locally
- Text Generation WebUI – User-friendly LLM interface
- Prompt Engineering Guide – Comprehensive prompting techniques
- OpenAI Prompt Engineering – Best practices for prompting
- LangChain – Framework for LLM applications
- LlamaIndex – Data framework for LLM apps
- Axolotl – LLM fine-tuning toolkit
- Unsloth – Fast and memory-efficient fine-tuning
- LoRA: Low-Rank Adaptation – Efficient fine-tuning technique
- QLoRA – Quantized low-rank adaptation
- Open LLM Leaderboard – Model performance comparisons
- HELM Benchmark – Holistic evaluation of language models
- LM Evaluation Harness – Standardized evaluation framework
- BigBench – Comprehensive evaluation suite
- vLLM – High-throughput LLM serving
- TensorRT-LLM – NVIDIA optimized inference
- OpenLLM – Production LLM serving platform
- LocalAI – Self-hosted OpenAI alternative
- CLIP – Vision-language understanding
- DALL-E – Text-to-image generation
- Whisper – Speech recognition and transcription
- Code Llama – Code generation and understanding
- Scaling Laws for Neural Language Models – Understanding model scaling
- Constitutional AI – Training helpful, harmless AI
- Retrieval-Augmented Generation – Combining retrieval with generation
- In-Context Learning – Few-shot learning capabilities
Leverage AI-powered educational technology to create, enhance, and deliver machine learning education. These platforms combine artificial intelligence with pedagogical expertise to automate course creation, generate interactive content, and personalize learning experiences for students studying ML and data science.
| Tool | Description | Best For | Key Features |
|---|---|---|---|
| Coursebox | AI quiz and course builder | Automated course generation | Content generation, quiz creation |
| CourseAI | E-learning authoring with AI | ML course development | Curriculum planning, content structuring |
| Lingio | AI-driven course creation | Technical training | Interactive lessons, progress tracking |
| Mindsmith | AI learning design platform | Corporate training | Adaptive learning, content personalization |
- Canvas – Comprehensive LMS with AI features
- Moodle – Open-source learning platform
- Teachable – Course creation and sales platform
- Thinkific – Online course platform with analytics
- Jupyter Books – Interactive computational books for ML
- Streamlit – Create interactive ML learning apps
- Gradio – Build ML demos for educational purposes
- Observable – Interactive notebooks for data science education
- Quizlet – AI-powered study tools and flashcards
- Kahoot! – Interactive quiz platform
- H5P – Interactive content creation
- Socrative – Student response system
- Loom – Screen recording for tutorials
- Camtasia – Video editing for education
- OBS Studio – Open-source broadcasting software
- Descript – AI-powered video editing
- Snorkel AI – Programmatic data labeling for ML education
- Label Studio – Data labeling platform
- Roboflow – Computer vision dataset management
- Weights & Biases – Experiment tracking for educational projects
- Codecademy – Interactive coding education
- DataCamp – Data science learning platform
- Brilliant – Interactive STEM learning
- Coursera Labs – Hands-on project-based learning
- GitHub Classroom – Distribute and collect coding assignments
- Replit for Education – Collaborative coding environment
- Google Colab – Cloud-based ML education platform
- Kaggle Learn – Free micro-courses in data science
I welcome contributions! Please see our Contributing Guidelines for details on how to:
- Add new resources
- Update existing links
- Suggest improvements
- Report broken links
When adding resources, please use this format:
- 🏷️ Categories
- 🔧 Tools/Frameworks
- 📖 Books/Documentation
- 🎥 Videos/Courses
- 🎯 Interactive/Demos
- 📚 Tutorials/Guides
This project is licensed under the MIT License – see the LICENSE file for details.
Special thanks to all the educators, researchers, and developers who created these amazing free resources that make AI education accessible to everyone.