Learning AI Links

Comprehensive list of learning materials for Artificial Intelligence.

With one list. Zero confusion. And no fluff Videos:

  1. LLM Introduction: https://www.youtube.com/watch?v=zjkBMFhNj_g

  2. LLMs from Scratch: https://www.youtube.com/watch?v=kCc8FmEb1nY

  3. Agentic AI Overview (Stanford): https://www.youtube.com/watch?v=sal78ACtGTc

  4. Building and Evaluating Agents: https://www.deeplearning.ai/short-courses/building-evaluating-advanced-agents/

  5. Building Effective Agents: https://www.anthropic.com/research/building-effective-agents

  6. Building Agents with MCP: https://www.youtube.com/watch?v=XpZpZ_vC-Yk

  7. Building an Agent from Scratch: https://www.youtube.com/watch?v=XpZpZ_vC-Yk

  8. Philo Agents: https://github.com/philo-agents/philo Repos

  9. GenAI Agents: https://github.com/NirantK/GenAI-Agents

  10. Microsoft's AI Agents for Beginners: https://github.com/microsoft/ai-agents-for-beginners

  11. Prompt Engineering Guide: https://github.com/dair-ai/Prompt-Engineering-Guide

  12. Hands-On Large Language Models: https://github.com/nlp-with-transformers/notebooks

  13. AI Agents for Beginners: https://github.com/microsoft/ai-agents-for-beginners

  14. GenAI Agents: https://github.com/NirantK/GenAI-Agents

  15. Made with ML: https://github.com/GokuMohandas/Made-With-ML

  16. Hands-On AI Engineering: https://github.com/m-m-m-m/hands-on-ai-engineering

  17. Awesome Generative AI Guide: https://github.com/steven2358/awesome-generative-ai

  18. Designing Machine Learning Systems: https://github.com/chiphuyen/machine-learning-systems-design

  19. Machine Learning for Beginners from Microsoft: https://github.com/microsoft/ML-For-Beginners

  20. LLM Course: https://github.com/mlabonne/llm-course Guides

  21. Google's Agent Whitepaper: https://goo.gle/agent-whitepaper

  22. Google's Agent Companion: https://goo.gle/agent-companion

  23. Building Effective Agents by Anthropic: https://www.anthropic.com/research/building-effective-agents

  24. Claude Code Best Agentic Coding practices: https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview

  25. OpenAI's Practical Guide to Building Agents: https://platform.openai.com/docs/guides/optimizing-llm-accuracyBooks:

  26. Understanding Deep Learning: https://udlbook.github.io/udlbook/

  27. Building an LLM from Scratch: https://www.manning.com/books/build-a-large-language-model-from-scratch

  28. The LLM Engineering Handbook: https://www.llmengineeringhandbook.com/

  29. AI Agents: The Definitive Guide - Nicole Koenigstein: https://www.packtpub.com/en-us/product/ai-agents-the-definitive-guide-9781835084960

  30. Building Applications with AI Agents - Michael Albada: https://www.manning.com/books/building-applications-with-ai-agents

  31. AI Agents with MCP - Kyle Stratis: https://www.manning.com/books/ai-agents-with-mcp

  32. AI Engineering: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ Papers

  33. ReAct: https://arxiv.org/abs/2210.03629

  34. Generative Agents: https://arxiv.org/abs/2304.03442

  35. Toolformer: https://arxiv.org/abs/2302.04761

  36. Chain-of-Thought Prompting: https://arxiv.org/abs/2201.11903 Courses:

  37. HuggingFace's Agent Course: https://huggingface.co/learn/agents-course/unit0/introduction

  38. MCP with Anthropic: https://glitch.com/@anthropic/mcp-quickstart

  39. Building Vector Databases with Pinecone: https://www.pinecone.io/learn/vector-database/

  40. Vector Databases from Embeddings to Apps: https://www.deeplearning.ai/short-courses/vector-databases-embeddings-applications/

  41. Agent Memory: https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autogen/

curator: Shraddha Bharuka @BharukaShraddha

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