Learn AI
360 of the best places to learn about AI, from first steps to research level. Hand-picked, mostly free, with every link checked. No affiliate links on this page.
Courses
A progression of reputable AI courses and lecture series, from nontechnical introductions to graduate level study.
Beginner and no maths
- Google AI Essentials
A practical introduction to AI concepts, prompting, and everyday uses that requires no previous experience.
- AI For Everyone
A nontechnical overview of AI terminology, project planning, workplace applications, and social questions.
- Generative AI for Everyone
An accessible introduction to generative AI, large language models, capabilities, limitations, and responsible use.
- CS50's Introduction to Programming with Python
A free introduction to Python programming that prepares learners for later machine learning and AI study.
- Google AI Literacy
A collection of introductory lessons for students, educators, families, and professionals learning AI concepts and responsible use.
- Introduction to Generative AI
A short course explaining generative AI basics, common applications, and how it differs from traditional machine learning.
- AI Academy
Free self paced courses covering AI fluency and practical use for students and higher education communities.
- AI for Beginners
A twelve week curriculum covering AI history, neural networks, computer vision, language, reinforcement learning, and ethics.
- Generative AI for Beginners
A twenty one lesson curriculum for building generative AI applications with explanations, videos, code, and translations.
- Prompt Engineering for ChatGPT
A practical introduction to writing effective prompts and using language models for common tasks.
Core machine learning
- Machine Learning Specialization
A three course foundation in supervised learning, neural networks, tree methods, unsupervised learning, recommender systems, and reinforcement learning.
- Machine Learning Crash Course
Interactive lessons and exercises covering core machine learning concepts, production systems, and modern model development.
- Machine Learning for Beginners
A twelve week project based curriculum covering classical machine learning with Python, quizzes, assignments, and translations.
- Kaggle Learn
Short hands on tutorials covering Python, data preparation, machine learning, deep learning, and model explainability.
- Machine Learning with Python
An applied course using Python libraries to build, evaluate, and improve common machine learning models.
- Artificial Intelligence
An Indian university course introducing search, knowledge representation, reasoning, learning, and other core AI methods.
- Practical Deep Learning for Coders
A code first course that teaches practical deep learning through projects using modern Python libraries.
- Made With ML
A production machine learning course covering data, modeling, testing, deployment, monitoring, and software engineering practices.
- Deep Learning Institute Training
Instructor led and self paced training in deep learning, accelerated computing, generative AI, and deployment.
- Get started with AI applications and agents on Azure
A Microsoft Learn path introducing AI applications, agents, model use, and responsible development on Azure.
Deep learning and generative AI
- Hugging Face LLM Course
A free course on transformers, datasets, tokenizers, fine tuning, and sharing models with the Hugging Face ecosystem.
- Hugging Face AI Agents Course
A practical course on agent fundamentals, tools, frameworks, evaluation, and deploying agent systems.
- Deep Learning Specialization
A multi course program covering neural networks, optimization, convolutional networks, sequence models, and practical deep learning.
- Neural Networks and Deep Learning
The first course in a specialization explaining neural network foundations, vectorized computation, and model implementation.
- Generative AI with Large Language Models
An applied course covering transformer architecture, fine tuning, evaluation, and deployment choices for large language models.
- IBM Generative AI Engineering Professional Certificate
A professional certificate covering generative AI application development, prompt engineering, retrieval, and model integration.
- Introduction to Artificial Intelligence
A catalog of university and industry AI courses with free audit options and paid certificates on selected programs.
- AWS Skill Builder AI learning
A catalog of AWS learning plans and courses covering machine learning, generative AI, data, and cloud deployment.
University lecture series
- MIT 6.034 Artificial Intelligence
Full MIT materials on search, constraint satisfaction, learning, neural networks, and probabilistic reasoning.
- MIT 6.036 Introduction to Machine Learning
MIT lectures and assignments covering supervised learning, classification, regression, representation, and optimization.
- MIT 6.S191 Introduction to Deep Learning
An annual MIT lecture series introducing deep learning through computer vision, language, generative models, and hands on labs.
- Stanford CS229 Machine Learning
A graduate machine learning course covering statistical learning, neural networks, unsupervised learning, and reinforcement learning.
- Stanford CS231n Deep Learning for Computer Vision
Graduate lectures and assignments on image classification, convolutional networks, visual recognition, and modern computer vision.
- Stanford CS224N Natural Language Processing with Deep Learning
A graduate course on neural language processing, transformers, representation learning, and current NLP research methods.
- Stanford CS25 Transformers United
A seminar series featuring researchers discussing transformer models, language models, and related applications.
- Stanford CS336 Language Modeling from Scratch
A hands on course covering the complete process of building language models, from data preparation through evaluation.
- UC Berkeley CS188 Artificial Intelligence
A full introductory AI course covering search, games, constraint satisfaction, probability, decision making, and machine learning.
- UC Berkeley CS285 Deep Reinforcement Learning
Graduate lectures, projects, and readings on deep reinforcement learning, imitation learning, and model based control.
- CMU 15-281 Artificial Intelligence
A university AI course covering search, games, Markov decision processes, Bayesian networks, and machine learning.
Further study and international options
- Google Machine Learning and AI courses
Google's official learning hub links beginner, intermediate, developer, and workplace AI training.
- Google Machine Learning Crash Course in Spanish
The Google machine learning course is available with Spanish interface and translated learning materials.
- Microsoft AI for Beginners in Hindi
The Microsoft beginner AI curriculum includes a Hindi translation alongside practical lessons and notebooks.
- Microsoft Generative AI for Beginners in Spanish
The Microsoft generative AI curriculum provides Spanish translations for lessons on language models, prompting, and application building.
Videos and podcasts
A varied set of trusted videos, lecture series, channels and podcasts for learning about artificial intelligence from first principles to current practice.
Explainers and fundamentals
- 3Blue1Brown
Animated lessons explain the mathematics behind neural networks, linear algebra, calculus and probability.
- StatQuest with Josh Starmer
Clear videos explain statistics, machine learning methods and data science concepts with simple visual examples.
- Two Minute Papers
Short videos summarize recent research in artificial intelligence, machine learning, graphics and robotics.
- AI Explained
Research focused videos discuss major model releases, papers, capabilities and limitations in accessible language.
- Yannic Kilcher
Technical paper reviews cover language models, computer vision, reinforcement learning and other research topics.
- Computerphile
Computer scientists explain artificial intelligence, algorithms, neural networks, security and computing ideas.
- Branch Education
Detailed animations explain computer hardware, chips and the physical systems used for modern AI.
- Crash Course Computer Science
A structured introductory series covers computing foundations that help learners understand artificial intelligence.
- Data School
Practical tutorials introduce Python, pandas, machine learning and model evaluation for new data practitioners.
- AI Explained on YouTube
A second official channel page collects accessible discussions of artificial intelligence news and research.
Lectures and structured series
- Carnegie Mellon University lectures
University talks and course recordings cover machine learning, robotics, language technologies and computer science.
- Practical Deep Learning for Coders
A project based course teaches practical deep learning using Python, PyTorch and modern development workflows.
- Learning From Data MOOC
Eighteen lectures develop core machine learning theory through supervised, unsupervised and reinforcement learning ideas.
- Andrej Karpathy
Long form lessons build neural networks and language models from basic mathematics through working code.
- DeepLearning.AI
Lessons and interviews introduce machine learning, generative AI, agents and practical model development.
- Hugging Face Learn
Free courses and learning paths cover transformers, datasets, reinforcement learning, computer vision and agents.
- Full Stack Deep Learning
Lectures and labs cover the complete process of building, deploying and maintaining machine learning products.
- MIT OpenCourseWare
Open course materials include lecture videos, assignments and notes for artificial intelligence and machine learning.
- Stanford Online
Stanford course recordings and online classes cover artificial intelligence, machine learning and related computer science.
Practical machine learning and engineering
- freeCodeCamp.org
Long free courses teach Python, data science, machine learning, deep learning and generative AI development.
- sentdex
Hands on Python tutorials build applications with machine learning, computer vision, reinforcement learning and finance data.
- Krish Naik
Tutorials and projects cover data science, machine learning, deep learning, natural language processing and MLOps.
- AssemblyAI
Developer tutorials explain speech recognition, large language models, embeddings and production AI systems.
- DataCamp
Short lessons and courses introduce Python, statistics, data analysis and machine learning workflows.
- Microsoft Developer
Developer sessions cover Azure AI, machine learning, responsible AI, copilots and application development.
- PyTorch
Official talks and tutorials show how to train, evaluate and deploy models with PyTorch.
- NVIDIA
Technical talks explain accelerated computing, generative AI, robotics, graphics and infrastructure for model development.
- Kaggle Learn
Short interactive lessons teach Python, data analysis, machine learning and practical model building.
- AI Engineering Podcast
Interviews and technical discussions focus on building and operating modern AI applications and infrastructure.
Research labs and global perspectives
- OpenAI
Official talks and demonstrations explain OpenAI research, products, safety work and developer practices.
- Google DeepMind
Research stories, videos and educational material introduce advances in models, science, robotics and AI safety.
- Google AI
Official articles, videos and learning material cover Google research, products and applications of artificial intelligence.
- Google Research
Research publications, talks and project pages cover machine learning, computer vision, language and responsible AI.
- Microsoft Research
Research pages and talks cover artificial intelligence, machine learning, systems, language and societal impacts.
- Meta AI
Research updates and technical resources cover open models, computer vision, language and embodied AI.
- IBM Research Artificial Intelligence
Research explainers and publications cover enterprise AI, foundation models, trustworthy systems and scientific applications.
- Dot CSV
Spanish language videos explain machine learning, generative AI, research developments and their social implications.
Podcasts and long form conversations
- Machine Learning Street Talk
Long technical interviews examine machine learning research, AI safety, philosophy and competing views.
- TWIML AI Podcast
Interviews and discussions cover machine learning research, engineering, business use and responsible deployment.
- Gradient Dissent
Researchers and practitioners discuss machine learning experiments, infrastructure, evaluation and industry practice.
- The Cognitive Revolution
Weekly interviews explore AI research, products, policy, education and the effects of automation.
- Dwarkesh Podcast
Deeply researched interviews examine artificial intelligence, science, history, economics and long term technological change.
- Eye on AI
A journalist led podcast covers artificial intelligence research, business, policy, society and global power.
- Practical AI
Conversations and tutorials focus on useful machine learning techniques, software tools and real world applications.
- The Gradient Podcast
Researchers discuss technical and interdisciplinary perspectives on artificial intelligence and machine learning.
- The 80,000 Hours Podcast
Selected episodes examine transformative AI, alignment, governance, risks and possible benefits for society.
- The AI Daily Brief
Frequent briefings summarize important artificial intelligence news, product changes, research and policy debates.
Newsletters and blogs
These newsletters, research blogs, independent writers, magazines, and news sites explain AI developments with useful context and evidence.
Newsletters and recurring briefings
- Import AI
A long running newsletter that summarizes AI research, policy, and technical developments with detailed commentary.
- One Useful Thing
Practical essays about how AI changes work, education, organizations, and everyday decision making.
- Sebastian Raschka’s Blog and Notes
Technical notes and explanations about machine learning, deep learning, and large language models.
- Ben’s Bites
A frequently updated briefing covering AI products, tools, companies, and notable industry developments.
- The Rundown AI
Short daily explanations of important AI news, product releases, and practical developments.
- Latent Space
Technical essays and interviews about AI engineering, agents, infrastructure, and research practice.
- Turing Post
Carefully edited analysis of machine learning research, AI systems, agents, infrastructure, and industry incentives.
- CAIS Publications
Free newsletters covering AI safety research, policy, industry news, and social impacts without a paywall.
- AI4India Weekly
A newsletter and media hub focused on India’s AI research, policy, adoption, and workforce.
- Indian AI Weekly
A human edited weekly summary of Indian AI funding, models, policy, people, and opportunities.
- CSET Newsletters
Research briefings on AI, emerging technology, national security, and public policy from Georgetown researchers.
- The Gradient
A research publication featuring technical and interdisciplinary essays about machine learning and AI’s social effects.
Company and lab research blogs
- Google DeepMind News
Research updates and accessible explanations from one of the world’s major AI laboratories.
- AI at Meta Blog
Research and engineering updates covering open models, computer vision, language, robotics, and generative AI.
- Google Research Blog
Research announcements and technical explanations spanning machine learning, algorithms, science, health, and responsible AI.
- Microsoft Research Blog
Research stories about machine learning, computing systems, language, responsible AI, and applied science.
- OpenAI Research
Research publications and explanations about frontier models, reasoning, multimodal systems, and AI safety.
- Anthropic Research
Research from teams working on alignment, interpretability, economics, societal impacts, and frontier red teaming.
- Hugging Face Blog
Open machine learning tutorials, model explainers, research summaries, and practical engineering guides.
- BAIR Blog
Research summaries from Berkeley teams working across machine learning, robotics, vision, language, and AI safety.
- Stanford HAI News
Research news and policy analysis about human centered AI from Stanford academics and practitioners.
- Apple Machine Learning Research
Technical papers and research updates from Apple teams working on machine learning and intelligent products.
- AWS Machine Learning Blog
Practical articles about deploying machine learning and generative AI systems with AWS services.
- Epoch AI
Data driven research on AI capabilities, compute, trends, benchmarks, and the economics of development.
- Artificial Intelligence
Charts and research explaining AI adoption, capabilities, risks, education, and effects using public data.
- Mistral News
News and technical announcements from a major European AI company building open and commercial models.
Independent writers and explainers
- Simon Willison’s Weblog
Clear, practical posts about large language models, prompt injection, open source tools, and AI engineering.
- Lil’Log
Detailed technical essays explaining modern machine learning methods, agents, language models, and reinforcement learning.
- Chip Huyen’s Blog
Engineering focused writing about machine learning systems, production practices, evaluation, and model development.
- Jay Alammar
Visual explanations that make transformers, embeddings, language models, and machine learning concepts easier to understand.
- Hamel Husain’s Blog
Practical writing about evaluating, debugging, and building reliable applications with language models.
- AI as Normal Technology
Evidence based analysis of AI claims, products, research, and social effects from Princeton researchers.
Magazines and serious news sites
- IEEE Spectrum AI
Engineering focused reporting, explainers, videos, and analysis about artificial intelligence and machine learning.
- Quanta Magazine AI
Independent science journalism that explains important AI connections to mathematics, computer science, and biology.
- Ars Technica AI
Detailed technology reporting that covers AI products, research, security, policy, and computing infrastructure.
- WIRED Artificial Intelligence
Long form journalism and reporting about AI research, companies, culture, policy, and society.
- TechCrunch AI
Current reporting on AI startups, funding, products, companies, and technology industry developments.
- The Guardian AI
News, investigations, opinion, and features about AI’s effects on technology, politics, work, and culture.
- Nature Machine Learning
Peer reviewed research and news about machine learning across science, engineering, medicine, and society.
- Scientific American AI
Science journalism explaining artificial intelligence research, applications, limitations, and wider consequences.
- The Information
Subscription business reporting with substantial coverage of AI companies, products, infrastructure, and investment.
- Brookings Artificial Intelligence
Research and policy analysis on AI governance, economics, security, labor, and public institutions.
Books
A carefully selected reading list spanning accessible introductions, practical engineering, mathematical foundations, and the social history and consequences of AI.
For general readers
- Artificial Intelligence: A Guide for Thinking Humans
An accessible account of what AI can do, where it fails, and how its history shapes current expectations.
- Hello World
A clear tour of algorithms in medicine, transport, justice, and everyday decisions, with attention to their limits.
- You Look Like a Thing and I Love You
Funny experiments explain neural networks, machine learning mistakes, and why systems often behave unlike their designers expect.
- Life 3.0
A broad, speculative guide to possible futures involving advanced AI, intelligence, work, war, and human values.
- Artificial Intelligence
An illustrated history of AI that explains major ideas, applications, controversies, and the field’s changing ambitions.
- Simply AI
A jargon-light visual introduction covering AI concepts, mathematics, applications, history, and ethical questions.
- The Master Algorithm
An engaging overview of the main machine learning traditions and the search for a general learning method.
- Artificial General Intelligence
Explains technical routes toward general intelligence and examines consciousness, risk, and social consequences.
- Artificial Unintelligence
Real-world stories show why technology cannot solve every human problem and why skepticism improves AI decisions.
- A Citizen’s Guide to Artificial Intelligence
A concise, balanced introduction to the moral, political, legal, and economic questions raised by AI.
For builders and technical practitioners
- Artificial Intelligence: A Modern Approach
The standard broad textbook on intelligent agents, search, reasoning, machine learning, perception, language, and robotics.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
A practical Python route through classical machine learning, neural networks, deep learning, and production-oriented exercises.
- Designing Machine Learning Systems
Covers data, training, deployment, monitoring, reliability, and the trade-offs behind maintainable machine learning systems.
- AI Engineering
A systems guide to building applications with foundation models, including evaluation, fine-tuning, retrieval, and serving.
- Generative Deep Learning
Builds generative models with code, covering variational autoencoders, GANs, transformers, and diffusion models.
- Deep Learning with Python
A code-first introduction to deep learning concepts and workflows using Python and Keras.
- Build a Large Language Model From Scratch
Walks through implementing a small language model, from tokenization and attention to pretraining and fine-tuning.
- Machine Learning Bookcamp
A project-based introduction to training, deploying, and serving machine learning models with practical Python examples.
- Applied Machine Learning and AI for Engineers
An applied guide to machine learning and deep learning for engineers, with Python examples and minimal mathematics.
- Deep Learning for Coders with fastai and PyTorch
A free, practical book that teaches useful deep learning models through fastai and PyTorch projects.
- Dive into Deep Learning
An interactive open textbook combining explanations, mathematics, executable code, and exercises across modern deep learning.
- Speech and Language Processing
The authors’ free online draft covers language technology from classical NLP through neural models and large language models.
Mathematical and statistical foundations
- Mathematics for Machine Learning
A free companion text developing the linear algebra, calculus, probability, and optimization used in machine learning.
- Deep Learning
A rigorous open reference on deep learning mathematics, representation learning, optimization, and practical architectures.
- Probabilistic Machine Learning
A free two-volume treatment of probabilistic modeling, statistical learning, deep learning, and decision-making.
- Understanding Deep Learning
An open textbook explains deep learning models with accessible mathematics, visual intuition, and worked derivations.
- An Introduction to Statistical Learning
A widely used gentle introduction to statistical learning, with theory, examples, and free R and Python editions.
- The Elements of Statistical Learning
A free advanced reference on statistical learning methods, including regularization, trees, ensembles, and unsupervised learning.
- Reinforcement Learning: An Introduction
The classic free online textbook on reinforcement learning, dynamic programming, temporal difference methods, and policy optimization.
- Information Theory, Inference, and Learning Algorithms
A mathematically rich treatment linking information theory, Bayesian inference, coding, and learning algorithms.
- Pattern Recognition and Machine Learning
A standard mathematical introduction to probabilistic models and machine learning methods for technically prepared readers.
- Algorithms for Decision Making
A free technical book on probabilistic inference, planning, reinforcement learning, and sequential decision-making.
AI, ethics, economics, and history
- Atlas of AI
Investigates the hidden labour, resources, politics, and environmental costs behind contemporary AI systems.
- Automating Inequality
Shows how automated eligibility and risk systems can deepen poverty and restrict access to public services.
- Unmasking AI
Combines research and personal experience to explain bias in facial analysis and the case for accountable AI.
- Data Feminism
A free online book offers principles for challenging power and inequality in data science and AI.
- Power and Progress
A long-run economic history arguing that technology benefits society only when its gains are broadly shared.
- The Technology Trap
Places AI and automation in the history of labour-displacing technologies and examines their distributional effects.
- AI Superpowers
Compares the AI ecosystems of China and the United States and discusses automation, work, and geopolitical power.
- The Age of AI
A policy-oriented discussion of how AI may affect knowledge, security, diplomacy, and human understanding.
- Rebooting AI
Argues that reliable AI needs common sense, reasoning, and hybrid methods beyond current deep learning systems.
- Superintelligence
A detailed philosophical and strategic analysis of possible machine superintelligence and the control problem.
- Human Compatible
Explains why AI objectives can conflict with human preferences and proposes a different foundation for beneficial systems.
- The Ethical Algorithm
Introduces technical approaches to fairness, privacy, transparency, and accountability in algorithmic decision-making.
Hands-on building
Build practical AI skills with official documentation, guided lessons, hosted notebooks, open models, and local development tools.
Provider documentation and cookbooks
- OpenAI Platform documentation
Official documentation for building applications with OpenAI models, APIs, tools, and platform features.
- OpenAI Cookbook
Practical examples for prompting, structured outputs, retrieval, agents, evaluation, and other API workflows.
- Claude Platform documentation
Official guides for using Claude through the API, including messages, tools, vision, and prompt design.
- Claude Cookbooks
Notebook recipes showing practical Claude applications such as retrieval, classification, and tool use.
- Gemini API documentation
Guides for getting started with Gemini models, SDKs, multimodal input, tools, and production features.
- Gemini API Cookbook
Runnable examples covering Gemini authentication, embeddings, file handling, function calling, and agent patterns.
- Mistral AI documentation
Documentation for Mistral models, APIs, agents, fine tuning, and deployment options.
- Cohere documentation
Developer guides for Cohere language models, embeddings, reranking, tool use, and enterprise applications.
- Cohere Cookbooks
Step by step examples for building language applications and evaluating generation quality with Cohere.
- Hugging Face LLM Course
A free course introducing transformers, datasets, tokenizers, model sharing, and practical language model work.
Courses, tutorials, and notebooks
- Machine Learning Crash Course
An interactive introduction to machine learning concepts with exercises, videos, and browser based practice.
- AI for Beginners
A twelve week curriculum with lessons, quizzes, labs, ethics, and translations for learners worldwide.
- Generative AI for Beginners
A practical lesson series covering generative AI concepts, prompting, applications, and responsible development.
- Practical Deep Learning for Coders
A code first deep learning course using modern tools to build useful models quickly.
- Kaggle Learn
Short hands on lessons covering Python, data analysis, machine learning, deep learning, and AI topics.
- PyTorch Learn the Basics
A guided introduction to tensors, datasets, model building, optimization, and saving PyTorch models.
- TensorFlow Tutorials
Notebook based tutorials for learning TensorFlow and Keras through beginner examples and advanced workflows.
- Scikit learn MOOC
A free course teaching machine learning fundamentals through scikit learn exercises and practical examples.
- Full Stack Deep Learning
Lectures and labs covering the complete process of building, deploying, and maintaining machine learning products.
- Stanford CS231n
Lecture notes and assignments for deep learning applied to computer vision and image recognition.
- Stanford CS224N
Course materials on natural language processing, neural networks, attention, transformers, and language models.
Hosted notebooks and free compute
- Google Colab
A hosted Jupyter notebook environment for running Python and machine learning experiments with optional accelerators.
- Kaggle Notebooks
Hosted notebooks with datasets, GPUs, competitions, and a community for sharing reproducible data projects.
- Lightning AI Notebooks
Cloud notebooks for developing and running AI code with managed environments and optional GPU access.
- Hugging Face Spaces
A platform for hosting and trying interactive machine learning demos built with popular open source libraries.
- Paperspace
Cloud infrastructure for notebooks, development environments, and GPU powered machine learning workloads.
- Saturn Cloud
Cloud workspaces for data science and machine learning with notebooks, jobs, and scalable compute.
- Deepnote
Collaborative data science notebooks for exploring data, writing code, and sharing reproducible analyses.
Model hubs, datasets, and open source frameworks
- Hugging Face Models
A searchable hub of community and organisation models with cards, metadata, demos, and download tools.
- Hugging Face Datasets
A hub for discovering, documenting, loading, and sharing datasets used in machine learning.
- Kaggle Datasets
A public collection of datasets for analysis, learning, notebooks, and machine learning projects.
- Hugging Face Papers
A browsable feed of research papers linked to summaries, code, models, and related community activity.
- PyTorch
An open source framework for building and training deep learning models with Python and related tools.
- TensorFlow
An open source platform for developing, training, and deploying machine learning models across environments.
- JAX
A numerical computing framework with automatic differentiation and accelerated array operations for research.
- Keras
A high level deep learning API designed for readable model development across multiple backends.
- scikit learn
A widely used Python library for classical machine learning, preprocessing, model selection, and evaluation.
- Transformers
Documentation for using pretrained transformer models across text, vision, audio, and multimodal tasks.
- LangChain
A framework for composing language model applications with prompts, tools, agents, retrieval, and integrations.
- LlamaIndex
A framework for connecting language models to private data through indexing, retrieval, agents, and workflows.
- Gradio
A Python library for making interactive machine learning demos and web interfaces with little code.
Playgrounds and local model tools
- Google AI Studio
A browser workspace for testing Gemini prompts, structured output, multimodal input, and API code generation.
- OpenAI Playground
A browser interface for experimenting with OpenAI models, prompts, parameters, tools, and generated code.
- Claude Platform
Anthropic’s developer console for trying Claude, managing API access, and exploring platform capabilities.
- HuggingChat
A browser chat interface for trying a changing selection of open models and assistant features.
- Ollama
A simple way to download and run open language models locally or through Ollama’s cloud service.
- LM Studio
A desktop application for discovering, downloading, and chatting with local language models.
- llama.cpp
An efficient C and C++ runtime for running and experimenting with language models on local hardware.
- vLLM
An open source serving engine for high throughput inference and production deployment of language models.
- Open WebUI
A self hosted web interface for interacting with local and remote language models.
- Jan
An open source desktop application for running language models locally and building private assistants.
Research and keeping up
These research indexes, lab pages, benchmarks, reports, and trackers help learners follow AI progress from first papers to current frontier results.
Paper search and discovery
- arXiv
Open repository for searching and reading current research papers across machine learning, artificial intelligence, and related fields.
- Hugging Face Daily Papers
Daily view of notable machine learning papers with links to papers, code, models, and community discussion.
- OpenReview
Open platform for conference submissions, peer reviews, rebuttals, and published papers in machine learning.
- Google Scholar
Broad academic search engine for papers, citations, authors, and related research across AI and other disciplines.
- ACL Anthology
Open digital archive for research papers and proceedings in natural language processing and computational linguistics.
- DBLP Computer Science Bibliography
Curated bibliography for computer science publications, authors, venues, and links to papers.
- OpenAlex
Open catalog of global scholarly works with search, filters, citation data, and a free research API.
- Connected Papers
Visual graph that helps you find papers related to a starting paper and trace earlier and later work.
- Litmaps
Literature mapping tool for discovering related papers, monitoring new publications, and organizing a review.
- ResearchRabbit
Paper discovery tool that maps connections between articles, authors, and topics for literature reviews.
- Elicit
Research assistant for finding papers, extracting study details, and organizing evidence around a question.
- Papers with Code via Hugging Face
Trending research view connecting machine learning papers with implementations, models, datasets, and tasks.
Research labs and publication pages
- Google DeepMind Research
Research, publications, project explainers, and technical updates from Google DeepMind teams.
- OpenAI Research
Research index covering frontier models, reasoning, multimodal systems, safety, and applied AI.
- Anthropic Research
Technical research and publications on language models, alignment, interpretability, and responsible deployment.
- Google Research Publications
Searchable publication index covering Google research in machine intelligence, perception, language, robotics, and more.
- Microsoft Research Publications
Searchable index of publications from Microsoft researchers and academic collaborators across many AI fields.
- Apple Machine Learning Research
Apple research page featuring papers, authors, and work in machine learning, computer vision, speech, and language.
- NVIDIA Research
Research portal covering computer vision, graphics, robotics, generative models, systems, and accelerated computing.
- Ai2
Nonprofit research institute sharing open AI models, datasets, tools, papers, and project results.
- Vector Institute Research
Canadian AI research and talent hub with research programs, publications, and collaboration opportunities.
- Stanford Center for Research on Foundation Models
Academic center studying foundation models through research, evaluations, datasets, and public educational resources.
Benchmarks and leaderboards
- MLCommons Benchmarks
Open benchmark suites measuring AI performance, efficiency, safety, and reliability across research and production systems.
- Arena Leaderboard
Human preference rankings based on anonymous model comparisons and user votes across several model categories.
- Artificial Analysis Model Leaderboards
Comparison tables for model quality, speed, pricing, context, and other practical measurements.
- LiveBench
Continuously refreshed benchmark designed to reduce contamination when comparing language model capabilities.
- LiveCodeBench
Contamination resistant coding benchmark using recently released programming contest and software engineering problems.
- MTEB Leaderboard
Standardized leaderboard comparing text embedding and reranking models across many languages and tasks.
- SWE-bench
Benchmark and leaderboard for evaluating models that resolve real software issues from GitHub repositories.
- BIG-bench
Collaborative benchmark collection for testing language model capabilities across diverse tasks and difficulty levels.
- Hugging Face Leaderboards and Evaluations
Directory and documentation for community and official model evaluations, leaderboards, and reproducible scoring.
- Frontier Benchmarks AI
Model comparison table aggregating published scores across reasoning, coding, knowledge, agent, and multilingual benchmarks.
- ARC Prize Leaderboard
Leaderboard for abstract reasoning systems evaluated on ARC benchmark tasks designed to test generalization.
Reports and progress datasets
- Epoch AI Data
Public datasets and explorers tracking AI models, benchmarks, compute, chips, companies, data centers, and usage.
- Epoch AI Models Dataset
Downloadable database tracking machine learning models, training compute, parameters, datasets, and publication dates.
- Stanford AI Index
Annual evidence based report tracking AI research, technical performance, investment, policy, education, and societal impact.
- Our World in Data Artificial Intelligence
Interactive charts and data exploring AI adoption, capabilities, investment, computing, and social effects.
- Our World in Data Scaling Up AI
Interactive analysis of how training data, compute, parameters, and model performance have changed over time.
- OECD.AI Policy Observatory
International policy observatory covering AI data, incidents, policies, trends, and national approaches.
- International AI Safety Report
Scientific review of general purpose AI capabilities, risks, evaluations, and risk management evidence.
- AI Incident Database
Searchable collection of documented real world harms and incidents involving artificial intelligence systems.
Ongoing research coverage
- TWIML AI Podcast
Interviews and discussions with researchers and practitioners about machine learning research and applications.
- Gradient Newsletter
Short weekly newsletter explaining important AI research and practical implications for technology professionals.
- Google Research
News, explainers, publications, tools, and research stories from Google teams working across science and AI.
- Hugging Face Blog
Technical articles and release notes about open models, datasets, evaluation, and machine learning practice.
- Samaritan Research Gradient Updates
Weekly commentary connecting new AI research with trends in capabilities, infrastructure, economics, and policy.
Safety, ethics and law
A practical selection of research organisations, ethics guides, official policy pages and legal trackers for understanding AI risks and governance.
AI safety research organisations and courses
- Center for AI Safety
A nonprofit conducting technical AI safety research, field building and public education about societal-scale risks.
- Center for Human-Compatible Artificial Intelligence
A research lab developing technical foundations for AI systems that remain beneficial, understandable and responsive to human interests.
- Alignment Research Center
A nonprofit research organisation studying theoretical and mechanistic approaches to aligning future machine learning systems.
- Apollo Research
An independent organisation researching scheming, evaluations, monitoring and governance for advanced AI systems.
- AISI Research and Publications
Published research on frontier AI evaluations, alignment, red teaming, control, misuse and societal resilience.
- AISI Research Agenda
The UK institute explains its research priorities, risk domains, evaluation methods and planned safety work.
- International AI Safety Report
An international scientific assessment of current evidence about advanced AI capabilities, risks and risk management.
- Safety and responsibility
An overview of OpenAI’s safety research, preparedness practices, evaluations and approaches to responsible deployment.
- Technical AI Safety Course
A facilitated course covering alignment, interpretability, evaluations, red teaming, control and scalable oversight.
- ARENA AI Safety Curriculum
Self-study materials covering deep learning, interpretability, reinforcement learning, evaluations and alignment science.
- MATS Research
A research and mentorship programme helping talented researchers work on technical AI safety projects.
- Redwood Research
A research organisation working on scalable oversight, interpretability and other technical approaches to AI alignment.
Ethics, rights and responsible AI
- Recommendation on the Ethics of Artificial Intelligence
The first global standard on AI ethics, covering human rights, fairness, transparency, oversight, sustainability and policy action.
- OECD AI Principles
International principles for trustworthy AI that promotes inclusive growth, human rights, transparency, robustness and accountability.
- AI Risk Management Framework
A voluntary framework and playbook for identifying, measuring and managing risks across an AI system’s lifecycle.
- Resource Library
A searchable library of research, practical tools and reports on fairness, transparency, workers, privacy and safe AI.
- Ethics in Technology Practice
Teaching materials with ethical lenses, decision frameworks, case studies, workshop slides and a toolkit for technologists.
- Ada Lovelace Institute
Independent research on how data and AI can work for people and society, with policy analysis and public participation.
- AI Now Institute
Research and analysis on the social, economic and political impacts of artificial intelligence and concentrated technology power.
- Algorithmic Justice League
Research, education and advocacy focused on documenting and reducing bias and harms in automated systems.
- Copyright and Artificial Intelligence
Official reports and notices on copyright questions involving generative AI, training data, outputs and digital replicas.
Government and regulator AI pages
- EU AI Act
The European Commission’s official overview of the AI Act, its risk-based rules, obligations, timeline and implementation materials.
- AI Act Service Desk
The EU’s practical information platform for navigating AI Act obligations, definitions, guidance and compliance questions.
- European AI Office
The European Commission explains the AI Office’s role in general-purpose AI, enforcement, standards, research and coordination.
- AI Safety Institute
The UK government hub for the AI Security Institute’s publications, research, news and official work.
- Artificial intelligence guidance
Regulatory guidance on data protection, fairness, explainability, security and individual rights when using AI.
- AI regulation, a pro-innovation approach
The UK white paper describing a principles-based, regulator-led approach to AI regulation and implementation.
- CAISSI
The US government centre for testing, measurement and standards work on security risks from highly capable AI systems.
- OECD AI Policy Observatory
A global policy observatory with AI principles, policy analysis, incidents, tools, metrics and country information.
- Artificial Intelligence
India’s official Ministry of Electronics and Information Technology page for national artificial intelligence initiatives and policy material.
- AI and Emerging Technologies Division
The Indian ministry’s division page covering AI committees, the national AI portal, IndiaAI Mission and policy work.
- Brazilian Artificial Intelligence Plan
The official 2024 to 2028 plan for developing and using AI responsibly in Brazil, with Portuguese and English materials.
- Brazilian AI Strategy repository
An official repository of Brazil’s AI strategy, legal instruments, reference documents, actions and governance records.
Legal trackers and policy monitoring
- Global AI Law and Policy Tracker
A regularly updated tracker of AI laws and policy developments across a selection of jurisdictions worldwide.
- OECD AI Incidents Monitor
A searchable evidence base tracking reported AI incidents and hazards to support risk analysis and policy learning.
- AI Regulation Tracker
A public database of AI regulations, enforcement actions, court decisions, definitions and official source links.
- AI Tracker
A country by country tracker of AI regulatory and policy developments across major technology markets.
- AI Governance Tracker
A comparative tracker covering AI regulatory approaches, principles and legal exposure across the Americas, APAC and EMEA.
- AI Law Center
A legal resource tracking evolving AI laws and regulatory developments in the United States, Europe and United Kingdom.
- DAIL, Database of AI Litigation
A broad database of ongoing and completed litigation involving AI, from algorithmic decisions to copyright and autonomous vehicles.
- Technology Law Litigation Tracker
A maintained tracker of US federal cases involving AI, training data, generated outputs, privacy, intellectual property and software.
- AI Lawsuit Tracker
A searchable database of major AI lawsuits, rulings and settlements across the United States, European Union and United Kingdom.
Communities and careers
Communities, events, competitions, funding programmes, job boards and career guidance for people building a career in AI.
Communities and discussion
- Hugging Face Forums
A busy forum for questions, projects, research discussion and practical help with open machine learning tools.
- Hugging Face Discord
The official Discord community for discussing models, projects, events and open source machine learning.
- DataTalks.Club
A global community offering discussions, Slack networking, events, mentorship and free courses for data and AI practitioners.
- MLOps Community
A practitioner community focused on deploying, evaluating and operating machine learning systems.
- r/MachineLearning
A large Reddit community for machine learning research, papers, technical questions and industry discussion.
- r/learnmachinelearning
A beginner friendly Reddit community for learning machine learning and getting advice on study projects.
- r/LocalLLaMA
A highly active community for running language models locally and discussing hardware, tools and model releases.
- Machine Learning on Stack Overflow
A searchable question and answer archive for implementing machine learning with common programming tools.
Meetups and conferences
- NeurIPS
A major international conference covering machine learning, neuroscience, artificial intelligence and related research.
- ICML
A leading annual conference for machine learning research, tutorials, workshops and community meetings.
- ICLR
An open research conference focused on representation learning, deep learning and related methods.
- AAAI Conference on Artificial Intelligence
A broad academic conference covering artificial intelligence research, applications, demonstrations and workshops.
- PyData
A worldwide network of community conferences and meetups about data science, analytics and machine learning.
- ODSC
A conference series with talks, training and networking covering data science, machine learning, LLMs and NLP.
- AI Tinkerers
A global meetup network where hands on AI builders demonstrate working projects and exchange practical advice.
- AI Loves Data
An enterprise AI community with events, webinars, podcasts and networking for people deploying practical systems.
- Deep Learning Indaba
An African community and conference supporting machine learning learning, research and connections across the continent.
- WiDS Worldwide
A global event and community supporting women and allies in data science through talks and local activities.
Competitions and challenges
- Kaggle Competitions
The largest competition platform for practicing data science and machine learning with public datasets and leaderboards.
- DrivenData Competitions
Competitions use real world data to address problems in health, climate, education and other social impact areas.
- AIcrowd
A platform for AI challenges, benchmarks and hackathons spanning language, vision, reinforcement learning and robotics.
- Zindi Challenges
An African founded platform hosting practical data science challenges, hackathons and competitions across many countries.
- EvalAI
An open source platform for evaluating and comparing machine learning algorithms through research challenges and benchmarks.
- Codabench
An open platform for running reproducible benchmarks and competitions with automated code evaluation.
- Tianchi Competitions
A large competition platform with data science challenges, tutorials and community activity from Alibaba Cloud.
- Hugging Face Competitions
A competition hub for machine learning tasks connected to the Hugging Face community and tools.
Fellowships and scholarships
- Google PhD Fellowship
A regional fellowship supporting promising PhD students doing innovative research in computer science and related fields.
- OpenAI Residency
A six month paid programme for builders and early researchers moving into practical AI research.
- Google DeepMind Education
Information about academic fellowships and scholarships supporting early career researchers in AI and AI enabled science.
- AI4ALL
A nonprofit offering education and pathways designed to broaden participation in artificial intelligence.
- Microsoft Research Academic Programs
A central directory of Microsoft Research fellowships, collaborations and programmes for academic researchers.
- Anthropic Fellows Program
A funded fellowship supporting technical people transitioning into empirical AI safety research with mentorship.
- Claude Corps Fellowship
A paid yearlong fellowship placing early career AI skilled people with mission driven nonprofit hosts.
- Vector Scholarship in AI
A merit based scholarship for students entering eligible AI related master’s programmes in Ontario.
- Mila Sustainability Scholarships
Scholarships supporting researchers applying artificial intelligence to climate and sustainability problems.
Job boards and career guides
- Foorilla Hiring
A curated board for technology, AI and remote jobs with tools for candidates and employers.
- Y Combinator Startup Jobs
A job board covering thousands of roles at YC backed startups, including many AI companies.
- Wellfound
A startup hiring marketplace where candidates can discover roles and apply directly to founders.
- Work at a Startup
A YC startup jobs board covering engineering, product, design and other roles.
- LinkedIn AI Jobs
A broad international job search for artificial intelligence roles with filters for location and experience.
- Microsoft Learn AI Engineer Career Path
An official learning path describing AI engineering work, skills, training options and certification preparation.
- How to Land a Job in Machine Learning or Data Science
Practical hiring advice from experienced technical recruiters covering roles, resumes, interviews and job searches.
- How to Build Your Career in AI
A free guide covering AI skills, projects, interviews, networking and choosing an organisation.
- Meta AI Join Us
An official overview of AI research roles, engineering opportunities and residency pathways at Meta.
- AI Jobs at Foorilla
A current hiring directory for technology roles including AI, software, research and remote work.
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