AI Guild — Learning Assets (2026)

A curated, builder-first repository of AI Guild resources: roadmaps, workshop decks, reference PDFs, and hands-on notebooks (Colab/Jupyter). The intent is simple: learn → build → publish proof.

What you’ll find here

• Roadmaps to move from AI-curious to AI builder (2026 focus)
• Workshop decks (AI Explorer sessions, CPE-ready material)
• RAG foundations + demos (production-minded patterns, not just toy examples)
• Hands-on notebooks (Python refreshers, sentiment analysis, LangChain + Chroma demos)

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Quick Start (Recommended Learning Order)

1. Roadmap (high-level direction)

   • DDS_2026_Roadmap_4jan.pdf
   • AI_Curious_to_AI_Builder_2026_Roadmap.pdf

2. AI Explorer decks (conceptual clarity + business translation)

   • AI_Explorer_Professional_CPE_Deck.pdf

3. RAG Foundations (core mental model + pipeline)

   • RAG foundation 6th Jan 2026.pdf

4. RAG Demo (hands-on implementation)

   • RAG_Demo_LangChain_Chroma_Colab.ipynb

5. Skill boosters (Python + applied notebook)

   • DDS_Python_mini_course2026.ipynb
   • Copy_of_DDS_Academy_LLM_Sentiment_Analysis.ipynb

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Repository Contents

Type	File	Purpose
PDF	AI_Curious_to_AI_Builder_2026_Roadmap.pdf	A practical pathway for turning “AI interest” into shipped demos and portfolio proof.
PDF	DDS_2026_Roadmap_4jan.pdf	DDS-wide roadmap and priorities for 2026.
PDF	AI_Explorer_Professional_CPE_Deck.pdf	Professional deck for AI Explorer sessions (CPE-friendly).
PDF	RAG foundation 6th Jan 2026.pdf	RAG fundamentals: ingest → chunk → embed → retrieve → generate, with quality + trust considerations.
Notebook	RAG_Demo_LangChain_Chroma_Colab.ipynb	End-to-end RAG demo using LangChain + Chroma (builder workflow).
Notebook	DDS_Python_mini_course2026.ipynb	Python essentials refresher for AI builders (fast-track).
Notebook	Copy_of_DDS_Academy_LLM_Sentiment_Analysis.ipynb	Applied LLM sentiment analysis notebook (practical baseline).

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How to Use

Option A — Read PDFs in GitHub

• Click any *.pdf file to open it in GitHub’s PDF viewer.
• For best viewing, download the PDF locally if you need full-screen reading or annotation.

Option B — Run Notebooks in Google Colab (recommended)

1. Open the notebook file in GitHub.
2. Click “Open in Colab” (if available) or copy the notebook URL into Colab:
   • In Colab: File → Open notebook → GitHub and paste your repo URL.

Tip: If you want a consistent “Open in Colab” button inside each notebook, add a small header cell with a GitHub/Colab badge.

Option C — Run Notebooks Locally (Jupyter)

Prerequisites

• Python 3.10+ recommended
• Jupyter installed (pip install jupyter)

Run

jupyter notebook