Summary

This repository provides a comprehensive, practical roadmap and resource guide for individuals aiming to enter the fields of Generative AI (GenAI) and Agentic AI. It serves as a curated learning path, consolidating essential knowledge and tools for aspiring AI/ML practitioners.

How It Works

The roadmap is structured as a detailed table of contents, progressing from foundational mathematics and Python to advanced topics like Deep Learning, NLP, MLOps, Transformers, LLMs, RAG, Vector Databases, and Agentic AI. Each section lists key concepts, provides brief descriptions, and links to curated external resources such as tutorials, documentation, courses, and research papers. Practical project suggestions with associated datasets and tech stacks are also included to facilitate hands-on learning.

Quick Start & Requirements

This repository is a learning resource and does not contain code to install or run. It outlines a curriculum for self-study in AI/ML. Prerequisites are general programming familiarity for Python basics, and a willingness to engage with external educational materials. Links to official documentation and tutorials for specific technologies are provided within the roadmap.

Highlighted Details

• Covers a broad spectrum of AI/ML, from core mathematical and programming foundations to cutting-edge areas like Agentic AI, LangGraph, and the Model Context Protocol (MCP).
• Features practical project ideas across different domains (Core ML, NLP, Deep Learning, LangChain, RAG, Agentic AI) with suggested tech stacks and datasets.
• Curates learning materials from reputable sources including 3Blue1Brown, Stanford CS229, Hugging Face, OpenAI, and official documentation for frameworks like PyTorch, TensorFlow, and LangChain.
• Includes in-depth sections on LLM advancements such as Parameter-Efficient Fine-Tuning (PEFT), quantization techniques (LoRA, QLoRA, GPTQ), and advanced agentic workflows.

Maintenance & Community

The roadmap is described as actively maintained and continuously updated with the latest developments in GenAI and Machine Learning. The author encourages community contributions, including adding new resources, suggesting improvements, sharing project experiences, and reporting broken links. No specific community channels (e.g., Discord, Slack) or notable contributors are listed.

Licensing & Compatibility

No software license is specified within the provided README content. Users should assume all rights are reserved unless external links or repository files indicate otherwise.

Limitations & Caveats

As a curated list of resources, the repository's utility is dependent on the quality, accuracy, and continued availability of the external links provided. It does not offer direct tooling or code execution capabilities. The "Description: None" field suggests a lack of formal project description outside the README.