The AI <span class="text-transparent bg-clip-text bg-gradient-to-r from-purple-400 to-cyan-400">Learning Hub.

Richard's Take: Start here. Every technology leader should understand what Claude can and cannot do before making procurement or build decisions.

Learn how to use Claude for everyday work tasks and explore resources for advanced learning.

AI Fluency: Framework & Foundations

Richard's Take: The best single course for board directors and C-suite executives. Covers the ethical and safety considerations that matter in governance conversations.

Learn to collaborate with AI systems effectively, efficiently, ethically, and safely.

Introduction to Claude Cowork

Richard's Take: Hands-on course for leaders who want to use Claude as a daily productivity tool — not just understand it theoretically.

Learn to work alongside Claude on your real files and projects with the Cowork task loop.

Google AI Essentials

Richard's Take: Google's entry-level AI course. Excellent complement to Anthropic's offering — gives you vocabulary from both major ecosystems.

Google

Learn how to use AI to boost productivity, from writing prompts to using AI tools for data analysis and content creation.

AI for Everyone

Richard's Take: The foundational course that started it all. Still the best for building executive-level AI intuition without any technical background.

Andrew Ng's famous non-technical course explaining what AI can and cannot do, covering strategy and social impact.

AI Transformation Playbook

Richard's Take: Read this before hiring your first ML engineer. It prevents the most common and expensive mistakes in AI adoption.

Landing AI

Andrew Ng's guide on how to lead an AI transformation in your organization.

Elements of AI

Richard's Take: The most-enrolled AI course in history. Great for board members who want a rigorous but accessible foundation.

University of Helsinki

A free online course for everyone interested in learning what AI is and how it will affect our lives — 750K+ students enrolled.

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For Builders & Developers

Integrate AI into your development workflow. Ship AI-powered features.

8 resources

AI Practitioner Certification Pass

Richard's Take: If you are shipping AI features, you must understand their unit economics before deployment. My flagship course designed specifically for engineers.

Complete practitioner access to all AI economics, systems mapping, and model arbitrage curriculum modules.

Building with the Claude API

Richard's Take: Essential for any developer building AI features. Pair this with my AUEB calculator to validate your unit economics before you ship.

Comprehensive course covering API requests, prompt design, and integrating Claude with external services.

Claude Code in Action

Richard's Take: The practical "get things done" course. If your team uses Claude Code, this is required training.

Integrate Claude Code into your development workflow — read files, run commands, edit code, and automate tasks.

Introduction to Agent Skills

Richard's Take: Skills are how you scale AI productivity across a team. Eliminates the "everyone prompts differently" problem.

Build, configure, and share Skills in Claude Code — reusable markdown instructions for tasks.

Introduction to Subagents

Richard's Take: Advanced but critical for complex codebases. Subagents keep main conversations clean while delegating specialized work.

Use and create sub-agents in Claude Code to manage context and delegate tasks.

ChatGPT Prompt Engineering for Developers

Richard's Take: Cross-platform prompt engineering fundamentals. The principles apply to Claude, GPT, and Gemini equally.

Learn prompt engineering best practices from OpenAI — applicable across all LLMs.

LangChain for LLM Application Development

Richard's Take: Useful for understanding orchestration patterns even if you don't use LangChain. The mental models transfer to any framework.

Build LLM-powered applications using LangChain's framework for chaining, memory, and agents.

Building Systems with the ChatGPT API

Richard's Take: The production engineering course. Covers patterns you'll need for any serious AI deployment: chains, evaluation, safety.

Build multi-step systems using large language models — chains of prompts, evaluation, and guardrails.

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For Architects & Infrastructure

Design production AI systems. MCP, cloud integrations, RAG, and infrastructure.

7 resources

Introduction to Model Context Protocol

Richard's Take: MCP is the emerging standard for AI-to-tool communication. Exogram uses MCP. This is the foundation course.

Build MCP servers and clients from scratch. Master tools, resources, and prompts.

MCP: Advanced Topics

Richard's Take: Production MCP deployments require understanding sampling and transport — this covers both.

Advanced implementation including sampling, notifications, file system access, and transport mechanisms.

Claude with Amazon Bedrock

Richard's Take: If your infrastructure runs on AWS, this is how you deploy Claude at enterprise scale.

Full training for working with Anthropic models through AWS Bedrock.

Claude with Google Cloud Vertex AI

Richard's Take: The GCP equivalent of the Bedrock course. Choose based on your cloud provider.

Working with Anthropic models through Google Cloud's Vertex AI.

Building RAG Agents with LLMs

Richard's Take: The enterprise RAG course. Covers retrieval, embedding, reranking, and optimization at scale.

NVIDIA

Build production RAG pipelines with NVIDIA's enterprise AI tooling.

LLMOps

Richard's Take: ML operations for LLMs. Essential for teams moving from prototype to production AI systems.

Learn to build LLM pipelines including data prep, training, and deployment with best practices.

Vector Databases: from Embeddings to Applications

Richard's Take: Understanding vector DBs is essential for RAG economics. This course covers the infrastructure you'll be paying for.

Comprehensive guide to vector databases, embedding models, and similarity search for AI applications.

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For Data Scientists & ML Engineers

Fine-tuning, evaluation, model comparison, and advanced ML techniques.

6 resources

Finetuning Large Language Models

Richard's Take: The ROI of fine-tuning is often misunderstood. Take this course, then use AUEB to validate whether fine-tuning is economically justified for your use case.

Learn when and how to fine-tune LLMs. Covers data preparation, training, and evaluation.

Evaluating and Debugging Generative AI

Richard's Take: Evaluation is where most AI projects fail. This course teaches you to catch problems before they reach production.

Learn systematic approaches to evaluating and debugging generative AI models.

Building Applications with Vector Databases

Richard's Take: Practical applications of vector databases beyond basic RAG. Worth taking after the architecture track.

Hands-on guide to building semantic search, RAG, and recommendation systems with vector databases.

Stanford CS229: Machine Learning

Richard's Take: The gold standard ML course. If you have time for only one deep technical course, this is it.

Stanford

Andrew Ng's legendary Stanford ML course. Covers supervised, unsupervised, and reinforcement learning foundations.

MIT 6.S191: Introduction to Deep Learning

Richard's Take: Condensed but rigorous. Great for ML engineers who want to understand the theory behind the APIs they're calling.

MIT

MIT's fast-paced deep learning course covering neural networks, CNNs, transformers, and generative models.

Hugging Face NLP Course

Richard's Take: The open-source AI ecosystem course. Essential if you're evaluating open-source vs. proprietary model decisions.

Hugging Face

Free NLP course covering transformers, fine-tuning, and deploying models with the Hugging Face ecosystem.

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For Product Managers

AI product strategy, user research with AI, and feature economics.

5 resources

AI Practitioner Certification Pass

Richard's Take: My 12-hour certification course showing PMs exactly how to calculate and defend AI feature profitability.

Complete practitioner access to all AI economics, systems mapping, and model arbitrage curriculum modules.

AI Product Management

Richard's Take: Start here if you're a PM building AI features. Understanding feasibility prevents expensive false starts.

Foundational AI concepts for product managers including feasibility analysis and team building.

How Business Thinkers Can Start Building AI Plugins

Richard's Take: Perfect for PMs who need to write specs for AI features without deep technical knowledge.

Non-technical guide to building AI plugins and understanding AI tool integrations.

AI-Powered Product Development

Richard's Take: The PM playbook for AI features. Covers prioritization, user research, and go-to-market for AI products.

Product School

Comprehensive guide on integrating AI into product development lifecycle — from ideation to deployment.

Pair Programming with a Large Language Model

Richard's Take: PMs who can prototype with AI ship 3x faster. This course makes you dangerous in the best way.

Learn to use LLMs as coding assistants — essential for PMs who want to prototype AI features.

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For Security & Governance

AI safety, red teaming, compliance, and responsible AI deployment.

4 resources

Red Teaming LLM Applications

Richard's Take: Essential before any production AI deployment. The cost of a security incident far exceeds the cost of red teaming.

Learn to identify vulnerabilities in LLM applications through systematic red teaming.

AI Safety Fundamentals

Richard's Take: The most thorough AI safety course available for free. Critical for teams building AI features with user-facing implications.

BlueDot Impact

Comprehensive AI safety curriculum covering alignment, interpretability, and governance.

Responsible AI Practices

Richard's Take: Google's responsible AI framework. Use this to build your internal AI governance policy.

Google

Google's guidelines for responsible AI including fairness, interpretability, privacy, and security.

NIST AI Risk Management Framework

Richard's Take: If you're in a regulated industry or selling to enterprise, you need to understand NIST AI RMF. It's becoming the baseline for AI compliance.

NIST

The U.S. government's AI risk management framework — the emerging compliance standard.

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For Educators & Nonprofits

Bring AI fluency into classrooms, teams, and organizations.

4 resources

AI Fluency for Educators

Richard's Take: If you're responsible for upskilling a team, start here — then use it as a template for your own training programs.

Empowers faculty and educational leaders to apply AI Fluency in teaching.

Teaching AI Fluency

Richard's Take: The "train the trainer" course. For organizations building internal AI education programs.

Empowers academic faculty to teach and assess AI Fluency in instructor-led settings.

AI Fluency for Students

Richard's Take: Recommend to junior engineers and new graduates. Sets the right foundation for AI-augmented work.

Develop AI Fluency skills that enhance learning, career planning, and academic success.

AI Fluency for Nonprofits

Richard's Take: Purpose-built for mission-driven organizations. Covers ethical considerations unique to nonprofit AI adoption.

Develop AI fluency to increase organizational impact while staying true to mission.

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For AI Economics & Strategy

AI cost analysis, unit economics, and strategic AI investment decisions.

4 resources

AI Unit Economics Benchmark (AUEB)

Richard's Take: My free tool. Run your numbers before you ship any AI feature. 73% of AI features are margin-negative.

Calculate the unit economics of your AI features — cost per interaction, margin analysis, and break-even points.

Product Economics Curriculum