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Beginner · 4 weeks
Generative AI Foundations
Build a practical foundation in large language models, prompting, evaluation, and responsible use — ready for workplace projects.
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AI Engineer
From Python fluency to production agents
A sequenced path for people who want to build RAG systems and AI agents, then talk about that work in interviews. Each step maps to a course, a lab, or a portfolio artifact.
Who it is for: Developers and data practitioners targeting AI Engineer / RAG / Agent roles
Sequence
- 1
Python
Comfort with scripts, APIs, and virtual environments. Bridged in early labs if you already code in another language.
- 2
Machine Learning orientation
Enough ML literacy to talk about evaluation, overfitting, and when classical ML still wins.
- 3
Generative AI
Foundations course: prompting, models, evaluation, and responsible use.
- 4
RAG
Enterprise RAG course: retrieval, citations, and evaluation of grounded Q&A.
- 5
LangChain
Composition patterns used in RAG and tool-using applications.
- 6
AI Agents
Agentic AI using Programming: tools, graphs, and deployment.
- 7
Deployment
FastAPI + Docker so a hiring manager can run your project.
- 8
Interview & placement
System-design style questions, portfolio narrative, and career-hub support.
Courses on this path
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Intermediate · 6 weeks
Retrieval-Augmented Generation (RAG)
Design enterprise knowledge assistants that retrieve from your documents, cite sources, and stay within policy.
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Intermediate–Advanced · 8 weeks
Agentic AI using Programming
Build multi-step AI agents with tools, memory, and orchestration — then deploy them with guardrails.
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