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.
build a product solo with AI, one-person SaaS with AI agents, AI agent product pipeline, indie hacker AI workflow, solo founder AI build stack, AI-first product development, build digital products with AI agents, agentic coding pipeline, one-person product business
practitioner
en
It is built for solo builders, indie hackers, and founders who want to ship a product without a team. It spans both business and engineering thinking and is set at a practitioner level, so it fits anyone already building or planning to build a product mostly by themselves.
Course Mode: online · Course Workload: PT100M · 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.
Use ChatGPT, Claude, and Gemini with confidence at work: learn the vocabulary, how models work, when to verify them, and reusable prompts. 8 chapters, foundations level.
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.