Deep Learning
A decision-framework deep learning course for engineers. Choose PyTorch vs TensorFlow, judge depth vs classical ML, weigh transfer learning, and reason about CNNs. 7 chapters.
AI literacy course, AI for professionals, how AI works explained, ChatGPT for work, when to trust AI, AI prompting basics, AI fundamentals course, Claude Projects setup, AI benchmarks explained, AI course for beginners
foundations
en
Course Mode: online · Course Workload: PT159M · Mode: online
A decision-framework deep learning course for engineers. Choose PyTorch vs TensorFlow, judge depth vs classical ML, weigh transfer learning, and reason about CNNs. 7 chapters.
Ship AI features to production: prompting, RAG, structured outputs, fine-tuning, and inference tuning. Hands-on, free, 12 chapters (~4.3h) for engineers.
Ship AI inside Microsoft Power Platform: AI Builder, Power Apps and Power BI Copilot, Dataverse agents, plus DLP governance. 6 chapters, ~2h, practitioner level.
Build real software with AI without writing code: custom assistants, AI agents, dashboards, and clickable prototypes. Hands-on with Claude Code, ChatGPT, and Lovable. 7 chapters, beginner.
Understand how tools, memory, and goals turn a chatbot into an AI agent that does work, why agents fail, and how to direct them. No code. 6 chapters, ~95 min, no experience needed.
Run AI directly on a phone or Mac with no cloud round-trip. Build with Apple Foundation Models, Gemini Nano, and MLX across 4 advanced chapters for app engineers.
Build and ship custom AI agents in Microsoft Copilot Studio: topics, RAG knowledge sources, connectors, actions, and DLP governance. 4 chapters, practitioner level.
Ship and operate AI features in production. The full LLMOps lifecycle across 11 chapters: testing, evals, deployment, observability, guardrails, cost, streaming, and load testing.