A free practical course covering the concepts, architecture, development practices, and engineering considerations involved in building production-ready AI agents.

Anthropic's educational platform for learning how to use Claude and develop effective workflows with generative AI and AI-assisted work.

An open-source AI coding agent that can work with codebases through an IDE, terminal, and development tools to assist with software engineering tasks.

Free courses and research resources focused on making deep learning practical, with hands-on lessons covering computer vision, NLP, tabular data, and model deployment.

Google's AI research organization producing research, publications, models, and technical work across artificial intelligence and machine learning.

Google's collection of machine learning learning materials covering foundational concepts, practical techniques, and resources for developers and data practitioners.

An AI platform and community ecosystem providing models, datasets, libraries, demos, and collaborative tools for building and sharing machine learning applications.

Practical courses and learning paths covering machine learning, NLP, computer vision, reinforcement learning, and modern AI development using Hugging Face tools.

A scientific review examining general-purpose AI capabilities, emerging risks, and approaches to AI safety and risk management.

A community hub where data scientists and machine learning practitioners discuss problems, share projects, exchange ideas, and learn from one another.

Practical data science challenges where participants build models and solutions against real-world datasets while developing skills through experimentation and competition.

A large collection of publicly available datasets that learners, researchers, and practitioners can explore and use for data analysis and machine learning projects.

Short, hands-on micro-courses covering Python, data visualization, machine learning, SQL, deep learning, and other practical data science skills.

Cloud-hosted computational notebooks for writing and executing Python or R code, exploring datasets, creating visualizations, and developing machine learning models.

An open-source framework for building applications and agents powered by language models, with integrations for models, tools, retrieval, databases, and agent workflows.

A practical introduction to machine learning from Google featuring explanations, visualizations, exercises, and coding practice covering core ML concepts and techniques.

Documentation for NumPy, the foundational Python library for numerical computing, providing multidimensional arrays, mathematical operations, and tools for scientific programming.

Self-paced courses and training materials covering artificial intelligence, deep learning, accelerated computing, data science, and NVIDIA technologies.

A startup support program offering eligible technology companies access to resources such as training, technical support, cloud credits, networking, and other development benefits.

An open-source platform for running and managing large language models locally, making it easier to experiment with and develop applications using local AI models.

Documentation and reference material for pandas, a Python library designed for data manipulation, cleaning, analysis, and working with structured datasets.

Documentation and learning material for Polars, a high-performance DataFrame library supporting data manipulation and analysis through Python, R, and Node.js APIs.

An open-source machine learning framework for building, training, evaluating, and deploying models across applications ranging from research to production systems.

Learning resources from the TensorFlow project covering machine learning fundamentals, model development, computer vision, NLP, and deploying TensorFlow applications.

An open-source headless browser automation CLI designed to allow AI agents to navigate websites and interact with web pages programmatically.