46 courses across 12 tracks
AcademAI is a public catalog of practical AI courses organized by use case, provider, and learning goal.
Begin with foundations, then move into provider-specific mastery when the goal is clear.
Scan ChatGPT, Microsoft Copilot, Perplexity, Grok, Ollama, Claude, APIs, MCP, and AI fluency tracks.
Use public overviews and the free AI Fluency foundation course before unlocking the paid mastery layer.
AcademAI's course catalog is a public map of practical AI training across provider-specific and goal-specific tracks.
Use it to compare ChatGPT workflows, Microsoft Copilot productivity, Perplexity research, Grok and xAI workflows, Ollama local AI labs, Claude and Claude Code, AI API development, Model Context Protocol, and AI fluency for work, education, students, and nonprofits.
Who the course is for, from first-time AI learners to workplace teams and technical builders.
What practical work the learner should be able to complete after the course.
Which beginner, workplace, or technical background helps the learner choose confidently.
How AcademAI uses public provider sources while writing original lessons and exercises.
Course summaries stay open for visitors comparing the catalog, and the AI Fluency foundation course is free before checkout.
Membership unlocks full lessons, scenario tests, progress tracking, AI syllabi, and certificates.
Each public overview explains who the course is best for, from new AI learners to builders and workplace teams.
Course summaries focus on practical work learners should be able to complete, not vague tool familiarity.
Prerequisite notes help visitors choose a beginner-friendly entry point or a more technical provider track.
AcademAI uses public provider documentation as source context, then writes original lessons, exercises, and review gates.
Topic hubs help visitors move from a broad AI training question to a focused course sequence. Each hub explains who it is for, what learners should be able to do, which courses fit, and what prerequisites matter.
Start with AcademAI's free AI Fluency foundation course before choosing a paid mastery path.
Learn how to use Claude for everyday work tasks, understand core features, and explore resources for more advanced learning.
Use Claude Code across terminal, IDE, desktop, and web workflows with clear project instructions, bounded permissions, sandbox checks, tests, diff review, and human approval.
Learn to work alongside Claude on your real files and projects. Hands-on coverage of the Cowork task loop, plugins, and workflows.
Build, test, and share Claude Code Skills as focused directories with a required SKILL.md, supporting resources, narrow tool access, and a reviewable evaluation loop.
Design and evaluate focused Claude Code subagents with file-based instructions, narrow tools, isolated context, explicit result contracts, safe parallel work, and human review.
Build a production-minded Claude API workflow with the Messages API, official SDKs, multimodal input, structured outputs, tool approval, batching, evaluation, and rollback.
First-of-its-kind training for integrating Claude models with AWS services through Amazon Bedrock.
Full spectrum of working with Anthropic models through Google Cloud's Vertex AI platform.
Build and verify a small MCP server and client with current protocol roles, stdio and Streamable HTTP transports, bounded capabilities, and explicit trust review.
Design, secure, observe, and migrate remote MCP services with current lifecycle behavior, Streamable HTTP, explicit authorization, bounded concurrency, and a tested rollback path.
Empowers faculty, instructional designers, and educational leaders to apply AI Fluency into teaching practice.
Empowers students to develop AI Fluency skills that enhance learning, career planning, and academic success.
Empowers academic faculty, instructional designers, and others to teach and assess AI Fluency in instructor-led settings.
Empowers nonprofit professionals to develop AI fluency to increase organizational impact while staying true to mission and values.
Classify an AI task by product surface, account or workspace, data boundary, current availability, execution owner, and human decision owner before work begins.
Use a generate-inspect-verify loop, then choose a chat, project, Custom GPT, skill, or reusable instruction package only after a current availability check.
Build workflow briefs for writing, marketing, education, small business, and nonprofit work with a decision owner, approved source package, output contract, critique pass, and approval gate.
Choose a Codex surface, define repository instructions and permission boundaries, then inspect, plan, edit, test, review, and hand off work with evidence.
Build reliable OpenAI API applications by choosing a direct or orchestrated path, keeping keys server-side, defining structured and multimodal data contracts, separating execution ownership, and shipping with evals and production controls.
Choose a Grok product surface by task, account, data boundary, current availability, and the human review the work requires.
Use Grok in team settings with account and workspace checks, approved context, privacy boundaries, adoption playbooks, and review ownership.
Use current Grok connector options and external integrations with minimum useful access, source boundaries, permission ownership, and review loops.
Build reliable xAI API workflows with server-side keys, Responses state choices, tool-execution boundaries, validated outputs, and production review gates.
Use documented Grok Build workflows with current release-status checks, repository inspection, scoped changes, tests, permissions, and handoff review.
Design source-checked Grok creative workflows across image, video, voice, and multimodal inputs with rights, consent, cost, and final-review boundaries.
Build a practical Perplexity foundation for answer-engine research, source trails, Learn Mode, privacy choices, and verification habits.
Design deeper Perplexity research workflows that compare sources, expose disagreement, check evidence quality, and produce decision-ready briefs.
Use Perplexity Projects for organized research, approved files and connectors, shared instructions, Computer tasks, collaboration, and maintained team knowledge.
Plan browser, cloud-task, and local-computer workflows with bounded access, source and action evidence, approvals, and rollback.
Build current Perplexity API integrations with Search API for ranked results, Agent API for new web-grounded or multi-step work, and Sonar only when an existing response contract still depends on it.
Design advanced Perplexity research-agent systems with MCP, embeddings, retrieval, source-aware agents, evaluation sets, and production security.
Choose the right Microsoft Copilot surface by checking product, account, license, grounding, permissions, available actions, and the human decision owner.
Use Microsoft 365 Copilot Chat for web-grounded drafting, summarizing, comparing, research, and planning while checking account, source, sensitivity, and review boundaries.
Design licensed Microsoft 365 Copilot workflows in Word, Excel, PowerPoint, Outlook, Teams, OneDrive, and SharePoint with signed-in-account, source, permission, and review checks.
Move beyond prompt tips into reusable Copilot prompt systems, source packages, critique passes, team prompt cards, and quality rubrics.
Plan governed Copilot adoption with licensing and metering, user and agent inventory, data policy, publishing controls, measured pilots, escalation, and retirement.
Design governed Copilot Studio agents with fit checks, instructions, knowledge, generative orchestration, tools, connection identity, consent, testing, escalation, and lifecycle controls.
Use GitHub Copilot across inline, chat, IDE agent mode, Copilot cloud agent, formerly called Copilot coding agent, CLI, GitHub.com, pull-request, Spaces, and MCP workflows with repository and human-review controls.
Build a dependable Ollama lab by checking model capabilities, machine fit, execution location, and privacy boundaries before each run.
Operate Ollama through the CLI and API with explicit installed, loaded, service, context, memory, residency, and troubleshooting checks.
Create inspectable Ollama model configurations with Modelfile directives, provenance, license review, fixed behavior tests, and model cards.
Build server-controlled Ollama applications with native generate, chat, and embedding routes plus explicit OpenAI-compatibility and cloud credential limits.
Build source-visible Ollama workflows with model-specific tool calling, structured output, thinking, web search, embeddings, RAG, and MCP controls.
Inspect ollama launch integrations and run agent workflows with explicit model, context, file, command, network, approval, rollback, and review boundaries.
Build an owned Ollama lab playbook for runtime evidence, visible cloud fallback, credentials, tool security, fixed evals, maintenance, and recovery.
