awesome-ai-llm-resources

# Learn AI Engineering A comprehensive collection of free resources to learn everything about AI/ML, LLMs and Agents. ## Mathematical Foundations - [Mathematics Roadmap for Machine Learning](https://thepalindrome.org/p/the-roadmap-of-mathematics-for-machine-learning) - [Essence of Linear Algebra - 3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) - [Probability & Statistics - Khan Academy](https://www.khanacademy.org/math/statistics-probability) - [Statistics Fundamentals - Josh Strarmer](https://www.youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9) - [Mathematics for Machine Learning Specialization - Coursera (Andrew Ng)](https://www.coursera.org/specializations/mathematics-machine-learning) ## Python - [AI Python for Beginners - Deeplearning.ai](https://www.deeplearning.ai/short-courses/ai-python-for-beginners/) ## AI & ML Fundamentals - [Machine Learning Crash Course - Google](https://developers.google.com/machine-learning/crash-course) - [AI for Beginners – Microsoft](https://microsoft.github.io/AI-For-Beginners/) - [Elements of AI – University of Helsinki](https://course.elementsofai.com/) - [Machine Learning Playlist - Josh Strarmer](https://www.youtube.com/playlist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF) - [Machine Learning Specialization - Coursera](https://www.coursera.org/specializations/machine-learning-introduction) ### Machine Learning Frameworks - [Scikit-learn](https://scikit-learn.org/stable/) - [XGBoost](https://xgboost.ai/) - [LightGBM](https://lightgbm.readthedocs.io/en/stable/) - [CatBoost](https://catboost.ai/) ## Deep Learning - [Deep Learning Specialization - Coursera (Andrew Ng)](https://www.coursera.org/specializations/deep-learning) - [Practical Deep Learning for Coders - Fast.ai](https://course.fast.ai/) - [Mathematics for Deep Learning](https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/) - [Deep Learning Playlist - Josh Starmer](https://www.youtube.com/playlist?list=PLblh5JKOoLUIxGDQs4LFFD--41Vzf-ME1) ### Deep Learning Frameworks - [TensorFlow](https://www.tensorflow.org/) - [PyTorch](https://pytorch.org/) - [Keras](https://keras.io/) ## Deep Learning Specializations ### Computer Vision - [Deep Learning for Computer Vision - Stanford](https://cs231n.stanford.edu/) ### Natural Language Processing (NLP) - [NLP Specialization - Coursera](https://www.coursera.org/specializations/natural-language-processing) ### Reinforcement Learning - [Deep RL Course - Hugging Face](https://huggingface.co/learn/deep-rl-course/unit0/introduction) - [Deep RL Bootcamp - UC Berkeley](https://sites.google.com/view/deep-rl-bootcamp/lectures) ## Generative AI - [The Building Blocks of Generative AI](https://shriftman.substack.com/p/the-building-blocks-of-generative) - [Generative AI for Beginners - Microsoft](https://github.com/microsoft/generative-ai-for-beginners) - [Generative AI for Everyone - Coursera](https://www.coursera.org/learn/generative-ai-for-everyone) ## Large Language Models (LLMs) - [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) - [Large Language Models explained briefly](https://www.youtube.com/watch?v=LPZh9BOjkQs) - [Intro to LLMs](https://www.youtube.com/watch?v=zjkBMFhNj_g&pp=ygUDbGxt) - [Understanding Large Language Models](https://magazine.sebastianraschka.com/p/understanding-large-language-models) - [A Visual Guide to Reasoning LLMs](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-reasoning-llms) - [Understanding Reasoning LLMs](https://magazine.sebastianraschka.com/p/understanding-reasoning-llms) - [Understanding Multimodal LLMs](https://magazine.sebastianraschka.com/p/understanding-multimodal-llms) - [A Visual Guide to Mixture of Experts (MoE)](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-mixture-of-experts) - [Finetuning Large Language Models](https://magazine.sebastianraschka.com/p/finetuning-large-language-models) - [How Transformer LLMs Work](https://www.deeplearning.ai/short-courses/how-transformer-llms-work/) - [Building GPT from scratch - Andrej Karpathy](https://www.youtube.com/watch?v=kCc8FmEb1nY) - [LLM Course - GitHub](https://github.com/mlabonne/llm-course) - [LLM Course - Hugging Face](https://huggingface.co/learn/llm-course/chapter1/1) - [Awesome LLM Apps - GitHub](https://github.com/Shubhamsaboo/awesome-llm-apps) ### LLM Chatbots - [ChatGPT](https://chatgpt.com/) - [Gemini](https://gemini.google.com/app) - [Claude](https://claude.ai/new) - [Perplexity](https://www.perplexity.ai/) ### Open Source LLMs - [Llama](https://www.llama.com/) - [Deepseek](https://chat.deepseek.com/) ### LLM APIs - [OpenAI](https://platform.openai.com/docs/overview) - [Anthropic](https://docs.anthropic.com/en/docs/overview) - [Gemini - Google](https://ai.google.dev/gemini-api/docs) - [Groq - Inference](https://groq.com/) ### LLM Tools & Frameworks - [LangChain](https://www.langchain.com/) - [LlamaIndex](https://www.llamaindex.ai/) - [Ollama](https://ollama.com/) - [Instructor](https://python.useinstructor.com/) - [Outlines](https://github.com/dottxt-ai/outlines) ### LLM Based IDEs - [Cursor](https://www.cursor.com/) - [Windsurf](https://windsurf.com/editor) - [GitHub Copilot](https://github.com/features/copilot) ### Agentic Coding Tools - [Claude Code](https://code.claude.com/docs/en/overview) - [Codex](https://openai.com/codex/) ## Prompt Engineering - [Google Prompting Essentials](https://www.coursera.org/google-learn/prompting-essentials) - [ChatGPT Prompt Engineering for Developers - Deeplearning.ai](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/) - [Advanced Prompting Techniques - Instructor](https://python.useinstructor.com/prompting/) - [Prompt Engineering Techniques - Github](https://github.com/NirDiamant/Prompt_Engineering) - [Getting Structured LLM Output - Deeplearning.ai](https://www.deeplearning.ai/short-courses/getting-structured-llm-output/) - [God Tier Prompts](https://www.godtierprompts.com/) ## Retrieval-Augmented Generation (RAG) - [Introduction to RAG - Coursera](https://www.coursera.org/projects/introduction-to-rag) - [RAG Techniques - Github](https://github.com/NirDiamant/RAG_Techniques) ## AI Agents - [A Visual Guide to LLM Agents](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents) - [Agents - Chip Huyen](https://huyenchip.com/2025/01/07/agents.html) - [AI Agents Course - Hugging Face](https://huggingface.co/learn/agents-course/) - [Building AI Browser Agents - Deeplearning.ai](https://www.deeplearning.ai/short-courses/building-ai-browser-agents/) - [GenAI Agents - Github](https://github.com/NirDiamant/GenAI_Agents) - [AI Agents in Action, Second Edition - Book](https://www.manning.com/books/ai-agents-in-action-second-edition) ## Model Context Protocol (MCP) - [MCP - Anthropic Guide](https://modelcontextprotocol.io/introduction) - [Building AI Apps using MCP](https://www.deeplearning.ai/short-courses/mcp-build-rich-context-ai-apps-with-anthropic/) - [MCP Course - Hugging Face](https://huggingface.co/learn/mcp-course/unit0/introduction) - [Awesome MCP Servers - Github](https://github.com/punkpeye/awesome-mcp-servers) ## MLOps & Deployment - [ML in Production - Coursera](https://www.coursera.org/learn/introduction-to-machine-learning-in-production) - [Full Stack Deep Learning](https://fullstackdeeplearning.com/course/2022/) - [ML System Design - Stanford](https://stanford-cs329s.github.io/syllabus.html) ### Tools - [Streamlit](https://streamlit.io/) - [MLflow](https://mlflow.org/docs/latest/index.html) ## Guides - [OpenAI Cookbook](https://cookbook.openai.com/) - [Anthropic courses](https://github.com/anthropics/courses/tree/master) ## Books - [Hands-On Machine Learning](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) - [Deep Learning - Ian Goodfellow](https://www.deeplearningbook.org/) - [Deep Learning with Python](https://www.amazon.in/Deep-Learning-Python-Francois-Chollet/dp/1617294438/) - [Why Machines Learn](https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749) - [Designing Machine Learning Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/) - [AI Engineering](https://www.oreilly.com/library/view/ai-engineering/9781098166298/) - [Build a LLM from Scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch) - [Prompt Engineering for LLMs](https://www.oreilly.com/library/view/prompt-engineering-for/9781098156145/) - [Natural Language Processing with Transformers](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/) - [Build a Multi-Agent System (from Scratch)](https://www.manning.com/books/build-a-multi-agent-system-from-scratch) - [Build a Reasoning Model (From Scratch)](https://www.manning.com/books/build-a-reasoning-model-from-scratch) - [Build an AI Agent (From Scratch)](https://www.manning.com/books/build-an-ai-agent-from-scratch) - [Build an LLM Application (from Scratch)](https://www.manning.com/books/build-llm-applications-from-scratch) - [AI Agents in Action](https://www.manning.com/books/gpt-agents-in-action) - [AI Agents in Action, Second Edition](https://www.manning.com/books/ai-agents-in-action-second-edition) - [LLMs in Production](https://www.manning.com/books/llms-in-production) ## YouTube Channels - [Andrej Karpathy](https://www.youtube.com/@AndrejKarpathy) - [3Blue1Brown](https://www.youtube.com/@3blue1brown) ## Other Resources - [Papers with Code](https://paperswithcode.com/) - [Kaggle Competitions](https://www.kaggle.com/competitions) ## Must-Read AI Papers - [Attention Is All You Need](https://arxiv.org/pdf/1706.03762) - [Generative Adversarial Networks (GANs)](https://arxiv.org/abs/1406.2661) - [GPT: Improving Language Understanding by Generative Pre-Training](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) - [GPT-3: Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) - [Chain-of-Thought Prompting Elicits Reasoning in LLMs](https://arxiv.org/abs/2201.11903)