viveknaskar/everything-ai-ml

A curated collection of learning resources for Generative AI, Machine Learning, Agentic AI, LLMs, RAG, Fine-tuning, MLOps, and more.

What it solves

Finding good, up-to-date learning material for AI and machine learning is a huge time sink. This project collects hundreds of hand-picked links into one place, covering everything from core ML concepts and math foundations to advanced GenAI, agentic AI, MLOps, security, and even quantum computing. It saves you from having to search across dozens of websites, blogs, courses, and GitHub repos on your own.

How it works

It's essentially a structured, browsable link directory. The README itself is a table of contents, each section containing links to articles, courses, videos, papers, and tools, each with a short description. The accompanying website (linked in the README) offers an interactive cheatsheet version of the same data. The README is auto-generated from a central source file (website/src/data/resources.ts) via a command, so the content stays consistent between the site and the README.

Who it’s for

Anyone learning or working in AI/ML—beginners wanting a structured roadmap, developers looking for practical tooling and framework docs, researchers seeking must-read papers, and even business leaders exploring GenAI adoption. It's also handy for educators and interview candidates looking for crash courses and practice problems.

Highlights

  • Covers a broad spectrum: key concepts, building blocks (math, Python, data preprocessing), a multi-step learning roadmap, GenAI general and advanced topics, prompt engineering, RAG, fine-tuning, frameworks (LangChain, LangGraph, CrewAI, Google ADK, Agno), agentic AI, MLOps/GenAIOps, security, Google Cloud AI, cost optimization, organizational adoption, quantum computing, AI-augmented SDLC, and more.
  • Includes interactive visualizations (CNN Explainer, Transformer Explainer, MLU-Explain) to make abstract concepts tangible.
  • Aggregates top-tier free resources: Stanford, MIT, Harvard, Google, OpenAI, Anthropic, Hugging Face, DeepLearning.AI, fast.ai, and others.
  • Curated list of research papers and must-read blogs for deeper dives.
  • Provides practice problems and interview preparation materials to test your skills.
  • Auto-generated from a central data source, keeping the site and README in sync.

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