Do I need prior experience with AI or machine learning?
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No. Phase 1 starts from setup and fundamentals, so you do not need machine-learning theory, model training, or previous AI project experience. What helps is comfort with basic programming—variables, functions, loops—and using a terminal.
What should I know before I start?
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Basic Python, or another language you can pick up quickly, plus a willingness to read documentation. Git, APIs, and command-line work appear early in Phase 1, and every module includes hands-on practice, so gaps get closed as you go rather than being assumed away.
How much time should I plan for each week?
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Plan for around 6–8 hours a week: short lessons plus the hands-on build for that module. Learners who can only give a few hours a week still finish, but the capstone benefits from a longer, uninterrupted stretch near the end.
How long does the complete course take?
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Most learners move through all three phases in roughly 12–16 weeks at a steady pace. The phases build in order—foundations, then orchestration, then production and the capstone—so the sequence matters more than the calendar.
How do I enroll, and where do I see pricing?
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Enroll from the course page; every Learn now button on this site opens it. Current pricing, payment options, and what your access includes are listed there, and the syllabus details every module before you decide.
Can I start with Phase 2 or Phase 3?
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The phases are ordered on purpose. Phase 2 assumes you can already build and call an application, retrieve context, and work with prompts. Phase 3 assumes you can orchestrate stateful workflows and evaluate them. If you already work with agents, the Phase 1 and Phase 2 module lists are the quickest way to check your gaps.
What happens in Phase 1: Foundations?
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Six modules that turn setup into working software: a Python and AI development environment, APIs and chat interfaces with FastAPI, Streamlit, and Gradio, LLM fundamentals and prompt engineering, embeddings and vector search, AI-assisted coding practice, and no-code workflow automation. You finish with several small applications you built yourself.
What happens in Phase 2: Orchestration & Protocols?
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Eight modules on turning isolated model calls into systems: LangChain chains, memory, and RAG; agents with typed tools; LangGraph workflows with routing, cycles, checkpoints, and human review; tracing, evaluation, and testing; the Model Context Protocol with custom MCP servers; and programmatic prompting. You finish with an orchestrated agent you can measure.
What happens in Phase 3: Production, Safety & Capstone?
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Eleven modules on making it real: deep agents with planning and long-term memory, multi-agent orchestration, agentic RAG and GraphRAG, visual automation, guardrails and safety, monitoring and fine-tuning, containerizing and deployment, agent interoperability, and LLM security. It closes with a two-part capstone you design, build, deploy, and document.