learn-ai-engineering

# 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](