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🎯 Learn AI/ML Interactively

I built AI-ML Companion - every AI, ML, GenAI and Agentic AI concept covered here, taught visually with animated diagrams, quizzes, and hands-on Python. The guides below are the full, continuously-updated versions of the reference material in this repo.

400+ modules β€’ 28 tracks β€’ 23 real-world projects β€’ free to start

Try AI-ML Companion

New Blog - Inside a Production Multi-Agent GenAI System

Popular guides: AI History β€’ Cheatsheets β€’ Interview Q&A β€’ Full Roadmap β€’ GenAI Imp Docs β€’ ML Project Interactive Visualization β€’ AI Blogs


Your go-to hub for end-to-end GenAI learning. ⭐ Star this repo to stay updated with the latest GenAI resources :)

πŸ“š Table of Contents


πŸ“– Documentation & Learning Resources

🎯 Getting Started

  • AI-ML Companion - Interactive AI/ML learning platform with 28 tracks, 400+ modules, visualizations, quizzes, and hands-on coding (ML fundamentals β†’ LLMs β†’ MLOps)
  • GenAI Roadmap - Your complete learning path for GenAI (interactive) Β Β·Β  markdown
  • AI/ML Roadmap - Comprehensive AI/ML learning Β Β·Β  PDF

🧠 Core Concepts

πŸ—οΈ Architecture & Technical Stack

☁️ Cloud Platform Guides

πŸ’Ό Career & Interview Preparation

Interview Q&A track - 20 always-current Q&A modules across ML, GenAI, and Agentic AI (free preview questions, full sets with Pro). The PDFs below are downloadable companions to these live modules.

πŸš€ Production & Enterprise


πŸ› οΈ Practical Use Cases & Projects

πŸ” Retrieval-Augmented Generation (RAG)

  • Advanced RAG - Comprehensive RAG techniques including agentic, graph, multimodal, and 9 advanced patterns (corrective RAG, hybrid search, query expansion, etc.)
  • Cache-Augmented Generation - Alternative to RAG using context caching for faster responses

πŸ€– Agentic AI & Orchestration

  • Agentic AI - Multi-agent systems with CrewAI & LangGraph frameworks
  • Tura - Local, open-source coding agent with CLI, TUI, web, and desktop interfaces for context-aware development workflows
  • AI Patterns - 23 advanced reasoning patterns (Chain-of-Thought, ReAct, Tree-of-Thought, Meta-Prompting, etc.)
  • MCP - Model Context Protocol - Standard protocol for LLM tool interoperability with web search
  • Multi-Agentic Prod Grade Content Moderation System - AI-Powered Multi-Agentic Content Moderation System with React Frontend
  • Handling Latency in Multi-Agentic System - How to handle Latency in Multi-Agentic System
  • agent-qa - Self-improving QA agent for natural-language web/mobile tests with persistent memory, MCP, and Agent Skills
  • YYLO - Command-line orchestrator for coding agents with typed task, validation, merge, and release-readiness boundaries in a dedicated branch/worktree
  • agent-manager - Terminal UI that runs coding-agent CLIs such as Claude Code, Codex and Gemini CLI side by side, each in its own persistent tmux session, with live status, git worktrees and diff review

πŸ’¬ Conversational AI

πŸ”§ LLM Providers & Tools

  • LLM Providers - Compare OpenAI, Gemini, Claude, Groq + local models (Ollama, HuggingFace)
  • Embedding Models - Guide to vector embeddings with Google, OpenAI, and HuggingFace

πŸ“Š Data & Analytics Applications

🎨 Prompt Engineering & Security

  • Prompt Engineering - 16+ techniques from basics to APE (Automatic Prompt Engineer)
  • Prompt Guard - Detect prompt injections and jailbreak attempts using Meta's Llama Guard

πŸ–ΌοΈ Multimodal & Specialized

⚑ Automation

  • n8n Automation - Setup and usage guide for n8n workflow automation platform

πŸ”— Quick Access Links

Category Resources
Learning Platform AI-ML Companion - Interactive AI/ML learning with 28 tracks, 400+ modules, quizzes & coding
Learning Path GenAI Roadmap β€’ AI/ML Roadmap
Cloud Platforms AWS β€’ Azure β€’ VertexAI
Interview Prep Interview Q&A track (20 modules) β€’ GenAI β€’ Agentic AI β€’ LLM
Popular Projects Advanced RAG β€’ Agentic AI β€’ Text-to-SQL

🀝 Contributing

Contributions are welcome. To add useful resources or code:

  1. Fork this repo

  2. Clone it

    git clone https://github.com/genieincodebottle/generative-ai.git
    
  3. Create a branch

    git checkout -b feature-name
    
  4. Make changes and commit

    git commit -m "Your message"
    
  5. Push your branch

    git push origin feature-name
    
  6. Open a Pull Request with a brief description of your changes.

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