A curated collection of learning resources for Generative AI, Machine Learning, Agentic AI, LLMs, RAG, Fine-tuning, and MLOps. It targets engineers and researchers seeking structured pathways and diverse materials to master modern AI concepts and tools, serving as a centralized hub for the rapidly evolving AI landscape.

How It Works

The project functions as an organized directory of links to external educational content, categorized by topic from foundational ML to advanced Generative AI and agentic systems. It emphasizes a mix of official documentation, academic courses, research papers, tutorials, and community discussions, providing a structured learning roadmap.

Quick Start & Requirements

This is a curated collection, not a runnable project. Setup involves navigating provided links. Prerequisites vary by resource but generally include Python, mathematics, and online platform access. An interactive cheatsheet is available at viveknaskar.github.io/everything-ai-ml.

Highlighted Details

• Extensive coverage: core ML, Generative AI, Agentic AI, LLMs, RAG, Fine-tuning, MLOps.
• Features interactive visual explainers for concepts like Transformers and CNNs.
• Includes curated lists of must-read research papers, influential books, and key tools/frameworks (e.g., LangChain, CrewAI).
• Provides structured learning paths and roadmaps, alongside prominent courses from Stanford, Coursera, and fast.ai.

Maintenance & Community

Community contributions are encouraged via pull requests. It links to external communities like Reddit (r/LearnMachineLearning) and Discord servers (Hugging Face, MLOps.org).

Licensing & Compatibility

The repository is for "educational purposes." All linked content belongs to their respective owners, governing usage. This collection imposes no software license; commercial compatibility depends on individual resource licenses.

Limitations & Caveats

Value depends on the currency and quality of linked external resources. As it doesn't host content, broken or outdated links are potential issues. The sheer volume may be overwhelming without clear personal learning objectives.