awesome-ai-engineer

# 🚀 Awesome AI Engineer Resources <div align="center"> # 🤖 Awesome AI Engineer ### A Curated Collection of **FREE** Resources to Become an **AI Engineer** From **Python** → **Machine Learning** → **Deep Learning** → **LLMs** → **Generative AI** → **RAG** → **AI Agents** → **LangChain** → **LangGraph** → **MCP** → **MLOps** → **Production AI Systems** ⭐ Learn from the world's best universities, AI companies, researchers, books, blogs, GitHub repositories, YouTube channels, documentation, and research papers.        </div> --- ## 🔥 Topics Covered `Python` • `Machine Learning` • `Deep Learning` • `Artificial Intelligence` • `Data Science` • `Generative AI` • `LLMs` • `GPT` • `ChatGPT` • `Claude` • `Gemini` • `Prompt Engineering` • `Embeddings` • `Vector Databases` • `Semantic Search` • `RAG` • `AI Agents` • `Agentic AI` • `LangChain` • `LangGraph` • `MCP` • `A2A` • `Agent Frameworks` • `Agent Protocols` • `AI Engineering` • `System Design` • `MLOps` • `Azure AI` • `Google AI` • `OpenAI` • `Anthropic` • `Hugging Face` --- # 📚 Table of Contents * 🎓 Free University Courses * 🏢 AI Companies * 🎯 Learning Platforms * 👨‍💻 AI Experts * 📺 YouTube Channels * 📖 Official Documentation * 💻 GitHub Repositories * 📚 Books * ✍️ Blogs * 📄 Research Papers * 🗺️ AI Engineer Roadmap * 🤖 [Agent: Frameworks & Protocols](./Agent-Frameworks-and-Protocols.md) * 🧠 [Embeddings Deep Dive](./Embeddings-Deep-Dive.md) --- # 🎓 Free University Courses Learn Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Mathematics, Algorithms, and Computer Science from the world's top universities. --- # 🏛 MIT OpenCourseWare **Tags** `Python` `Algorithms` `Computer Science` `Artificial Intelligence` `Machine Learning` `Linear Algebra` `Mathematics` `Probability` `Statistics` ### 🌐 Website https://ocw.mit.edu/ ### ▶️ YouTube https://www.youtube.com/@mitocw ### 📚 Recommended Courses * Introduction to Computer Science (6.0001) * Introduction to Algorithms * Mathematics for Computer Science * Linear Algebra * Probability & Statistics * Artificial Intelligence --- # 🌲 Stanford University **Tags** `Machine Learning` `Deep Learning` `Computer Vision` `NLP` `Transformers` `LLMs` `AI` ### 🌐 Website https://stanford.edu ### ▶️ YouTube https://www.youtube.com/@stanford ### 📚 Recommended Courses * CS229 — Machine Learning * CS231n — Deep Learning for Computer Vision * CS224N — Natural Language Processing with Deep Learning * CS25 — Transformers United * CS221 — Artificial Intelligence --- # 🧠 Carnegie Mellon University (CMU) **Tags** `Artificial Intelligence` `Machine Learning` `Deep Learning` `NLP` `Computer Vision` `Robotics` ### 🌐 Website https://www.cmu.edu ### ▶️ YouTube https://www.youtube.com/@CarnegieMellonUniversity ### 📚 Recommended Courses * Machine Learning * Deep Learning * Natural Language Processing * Artificial Intelligence --- # 🐻 UC Berkeley **Tags** `Machine Learning` `Deep Learning` `Artificial Intelligence` `Reinforcement Learning` `Data Science` ### 🌐 Website https://www.berkeley.edu/ ### ▶️ YouTube https://www.youtube.com/@UCBerkeley ### 📚 Recommended Courses * CS188 — Artificial Intelligence * CS285 — Deep Reinforcement Learning * Data100 — Principles of Data Science * CS189 — Machine Learning --- # 🎓 Harvard University **Tags** `Python` `Computer Science` `Artificial Intelligence` `Machine Learning` `Data Science` ### 🌐 Website https://pll.harvard.edu ### ▶️ YouTube https://www.youtube.com/@Harvard ### 📚 Recommended Courses * CS50 * CS50 AI * Data Science --- ## 🎯 Why Learn from Universities? These universities publish world-class AI and Computer Science courses completely free. They provide strong foundations in: * ✅ Python Programming * ✅ Data Structures & Algorithms * ✅ Mathematics for AI * ✅ Probability & Statistics * ✅ Machine Learning * ✅ Deep Learning * ✅ Computer Vision * ✅ Natural Language Processing * ✅ Reinforcement Learning * ✅ Large Language Models (LLMs) * ✅ Artificial Intelligence Research --- # 🏢 AI Companies Learn directly from the companies building today's most advanced **Large Language Models (LLMs)**, **Generative AI**, **AI Infrastructure**, **Foundation Models**, **Developer Platforms**, and **AI APIs**. --- # 🤖 OpenAI > Creator of **ChatGPT**, **GPT-4o**, **Codex**, **Embeddings API**, **Assistants API**, and the **OpenAI API**. ### 🏷️ Tags `ChatGPT` • `GPT-4o` • `Codex` • `OpenAI API` • `Embeddings` • `Function Calling` • `Structured Outputs` • `Agents SDK` • `Prompt Engineering` • `RAG` • `Fine-tuning` ### 🌐 Official Resources | Resource | Link | | ----------------- | ----------------------------------------- | | Website | https://openai.com | | API Documentation | https://platform.openai.com/docs | | Cookbook | https://github.com/openai/openai-cookbook | | GitHub | https://github.com/openai | | Blog | https://openai.com/news | | Platform | https://platform.openai.com | ### 📖 Learn * OpenAI API * Chat Completions * Responses API * Function Calling * Structured Outputs * Embeddings * Image Generation * Audio APIs * Fine-tuning * Retrieval-Augmented Generation (RAG) * AI Agents --- # 🧠 Anthropic > Creator of **Claude**, **Model Context Protocol (MCP)**, and AI systems focused on safety, reasoning, and long-context capabilities. ### 🏷️ Tags `Claude` • `MCP` • `Constitutional AI` • `Long Context` • `Prompt Engineering` • `AI Safety` • `Tool Use` • `Reasoning` ### 🌐 Official Resources | Resource | Link | | ------------- | ------------------------------ | | Website | https://www.anthropic.com | | Documentation | https://docs.anthropic.com | | GitHub | https://github.com/anthropics | | Blog | https://www.anthropic.com/news | ### 📖 Learn * Claude API * Prompt Engineering * Tool Use * MCP (Model Context Protocol) * Long Context Windows * AI Safety * Reasoning Models --- # 🌟 Google AI > Explore **Gemini**, **Vertex AI**, **Google AI Studio**, **TensorFlow**, and Google's cutting-edge AI research. ### 🏷️ Tags `Gemini` • `Vertex AI` • `Google AI Studio` • `TensorFlow` • `TPU` • `Multimodal AI` • `Agents` ### 🌐 Official Resources | Resource | Link | | --------------- | ---------------------------------- | | Website | https://ai.google | | Gemini API | https://ai.google.dev | | Vertex AI | https://cloud.google.com/vertex-ai | | Google AI Blog | https://blog.google/technology/ai | | Google Research | https://research.google | | GitHub | https://github.com/google | ### 📖 Learn * Gemini API * Prompt Design * Vertex AI * TensorFlow * AI Studio * Responsible AI * Agent Development --- # ☁️ Microsoft AI > Build enterprise AI applications with **Azure AI**, **Azure OpenAI Service**, **Copilot**, and **Azure AI Foundry**. ### 🏷️ Tags `Azure AI` • `Azure OpenAI` • `Copilot` • `Azure AI Foundry` • `Semantic Kernel` • `Azure ML` ### 🌐 Official Resources | Resource | Link | | ---------------- | ---------------------------------------------- | | Microsoft AI | https://www.microsoft.com/ai | | Azure AI | https://learn.microsoft.com/azure/ai-services/ | | Azure AI Foundry | https://learn.microsoft.com/azure/ai-foundry/ | | Semantic Kernel | https://learn.microsoft.com/semantic-kernel | | GitHub | https://github.com/microsoft | | Microsoft Learn | https://learn.microsoft.com | ### 📖 Learn * Azure OpenAI * Azure AI Search * Azure AI Foundry * Semantic Kernel * Copilot Development * Prompt Flow * Enterprise AI Solutions --- # 🚀 NVIDIA AI > Learn GPU computing, accelerated AI, inference optimization, and production deployment with NVIDIA's AI ecosystem. ### 🏷️ Tags `CUDA` • `TensorRT` • `NeMo` • `NIM` • `Triton` • `Inference` • `GPU` • `AI Infrastructure` ### 🌐 Official Resources | Resource | Link | | ----------- | -------------------------------------- | | Website | https://developer.nvidia.com/ai | | NVIDIA DLI | https://www.nvidia.com/en-us/training/ | | NVIDIA Blog | https://developer.nvidia.com/blog | | GitHub | https://github.com/NVIDIA | | CUDA | https://developer.nvidia.com/cuda-zone | ### 📖 Learn * CUDA Programming * GPU Computing * TensorRT * Triton Inference Server * [NVIDIA AI Engineer & Solutions Architect Skillset](./NVIDIA-AI-ENGINEER-SKILLSET.md) * NVIDIA NeMo * AI Infrastructure * Model Optimization --- # 🔵 Meta AI > Home of **Llama**, **PyTorch**, **FAIR**, and open research in generative AI and multimodal models. ### 🏷️ Tags `Llama` • `PyTorch` • `FAIR` • `Computer Vision` • `Open Source AI` • `Multimodal` ### 🌐 Official Resources | Resource | Link | | -------- | ----------------------------------- | | Meta AI | https://ai.meta.com | | Llama | https://ai.meta.com/llama/ | | PyTorch | https://pytorch.org | | FAIR | https://ai.meta.com/research | | GitHub | https://github.com/facebookresearch | ### 📖 Learn * Llama Models * PyTorch * Computer Vision * Multimodal AI * Open Source Foundation Models --- # 🤗 Hugging Face > The largest open-source AI ecosystem for models, datasets, inference, evaluation, and AI collaboration. ### 🏷️ Tags `Transformers` • `Datasets` • `Spaces` • `Diffusers` • `Inference API` • `Sentence Transformers` • `PEFT` • `TRL` ### 🌐 Official Resources | Resource | Link | | ------------- | ------------------------------- | | Website | https://huggingface.co | | Documentation | https://huggingface.co/docs | | Courses | https://huggingface.co/learn | | Models | https://huggingface.co/models | | Datasets | https://huggingface.co/datasets | | Spaces | https://huggingface.co/spaces | | GitHub | https://github.com/huggingface | | Blog | https://huggingface.co/blog | ### 📖 Learn * Transformers * Tokenizers * Diffusers * PEFT * TRL * Sentence Transformers * Hugging Face Hub * Inference API * Model Fine-tuning * Dataset Management --- # 📌 Why Learn from AI Companies? These organizations are shaping the future of Artificial Intelligence. Their official documentation, SDKs, APIs, open-source projects, blogs, and research papers are some of the best free learning resources available. By learning directly from these companies, you'll gain practical experience with: * ✅ Foundation Models (GPT, Claude, Gemini, Llama) * ✅ AI APIs & SDKs * ✅ Prompt Engineering * ✅ Retrieval-Augmented Generation (RAG) * ✅ AI Agents & Tool Calling * ✅ Model Context Protocol (MCP) * ✅ Embeddings & Vector Search * ✅ Fine-tuning * ✅ Multimodal AI * ✅ Production AI Systems * ✅ Enterprise AI Architecture * ✅ AI Infrastructure & Deployment --- # 🎯 Learning Platforms & 👨‍💻 AI Experts Learn Artificial Intelligence, Machine Learning, Deep Learning, Large Language Models (LLMs), Generative AI, Prompt Engineering, RAG, AI Agents, and MLOps from world-renowned educators, researchers, and practitioners. --- # 🎓 Learning Platforms These platforms provide **free courses**, **hands-on projects**, **coding notebooks**, **workshops**, and **industry best practices**. --- # 🧠 DeepLearning.AI > Founded by **Andrew Ng**, DeepLearning.AI offers some of the world's most popular AI and Generative AI courses. ### 🏷️ Tags `Machine Learning` • `Deep Learning` • `LLMs` • `Generative AI` • `Prompt Engineering` • `RAG` • `AI Agents` • `MLOps` ### 🌐 Official Resources | Resource | Link | | ------------------ | ----------------------------------------- | | Website | https://www.deeplearning.ai | | Free Short Courses | https://www.deeplearning.ai/short-courses | | YouTube | https://www.youtube.com/@Deeplearningai | | Newsletter | https://www.deeplearning.ai/the-batch | ### 📖 Recommended Courses * AI Python for Beginners * ChatGPT Prompt Engineering * Building Systems with ChatGPT * LangChain for LLM Application Development * LangGraph for Agentic AI * Building Agentic RAG * AI Agents in LangGraph * Building AI Products * Vector Databases * Generative AI with LLMs ### 💡 Why Learn Here? * Industry-recognized instructors * Hands-on notebooks * Free short courses * Latest Generative AI content * Practical LLM applications --- # ⚡ fast.ai > Practical Deep Learning for Coders by **Jeremy Howard**. ### 🏷️ Tags `PyTorch` • `Deep Learning` • `Computer Vision` • `NLP` • `Tabular Data` • `Production AI` ### 🌐 Official Resources | Resource | Link | | ----------- | ---------------------------------- | | Website | https://www.fast.ai | | Free Course | https://course.fast.ai | | GitHub | https://github.com/fastai | | YouTube | https://www.youtube.com/@fastdotai | ### 📖 Recommended Courses * Practical Deep Learning for Coders * From Deep Learning Foundations to Stable Diffusion * Neural Networks * Computer Vision * NLP * Recommendation Systems ### 💡 Why Learn Here? * Project-first approach * PyTorch focused * Production-ready examples * Excellent for practitioners --- # 👨‍💻 AI Experts Learn directly from researchers, engineers, and educators who have shaped modern Artificial Intelligence. --- # 🧑‍🏫 Andrew Ng > AI Pioneer, Founder of DeepLearning.AI, Co-founder of Coursera, Former Head of Google Brain. ### 🏷️ Tags `Machine Learning` • `Deep Learning` • `AI Strategy` • `Generative AI` • `LLMs` ### 🌐 Official Resources | Resource | Link | | --------------- | ------------------------------------- | | Website | https://www.andrewng.org | | DeepLearning.AI | https://www.deeplearning.ai | | YouTube | https://www.youtube.com/@AndrewNgAI | | LinkedIn | https://www.linkedin.com/in/andrewyng | ### 📚 Recommended Learning * Machine Learning Specialization * AI for Everyone * Generative AI for Everyone * Deep Learning Specialization * AI Leadership ### ⭐ Best For * Beginners * Machine Learning * AI Fundamentals * AI Career Guidance --- # 🤖 Andrej Karpathy > Former Director of AI at Tesla and Founding Member at OpenAI. ### 🏷️ Tags `LLMs` • `Transformers` • `GPT` • `Neural Networks` • `Tokenization` ### 🌐 Official Resources | Resource | Link | | ----------- | --------------------------------------- | | Website | https://karpathy.ai | | GitHub | https://github.com/karpathy | | YouTube | https://www.youtube.com/@AndrejKarpathy | | X (Twitter) | https://x.com/karpathy | ### 📚 Must-Watch Videos * Neural Networks: Zero to Hero * Let's Build GPT * Let's Build GPT Tokenizer * Let's Reproduce GPT-2 * State of GPT ### ⭐ Best For * Transformers * LLM Internals * Tokenization * Neural Networks * Building GPT from Scratch --- # 🚀 Jeremy Howard > Founder of fast.ai and one of the world's leading educators in practical Deep Learning. ### 🏷️ Tags `Deep Learning` • `PyTorch` • `Computer Vision` • `NLP` • `AI` ### 🌐 Official Resources | Resource | Link | | -------- | ---------------------------------- | | Website | https://www.fast.ai | | GitHub | https://github.com/jph00 | | YouTube | https://www.youtube.com/@fastdotai | ### 📚 Recommended Content * Practical Deep Learning for Coders * Stable Diffusion * Diffusion Models * Deep Learning Foundations ### ⭐ Best For * Practical AI * Deep Learning * PyTorch * Production ML --- # 🧠 Yann LeCun > Chief AI Scientist at Meta and Turing Award Winner. ### 🏷️ Tags `Deep Learning` • `Computer Vision` • `Self-Supervised Learning` • `AI Research` ### 🌐 Official Resources | Resource | Link | | -------- | ----------------------------------- | | Website | https://yann.lecun.com | | Meta AI | https://ai.meta.com | | Facebook | https://www.facebook.com/yann.lecun | ### 📚 Recommended Topics * Deep Learning * Self-Supervised Learning * Computer Vision * AI Research * Future of AI ### ⭐ Best For * AI Research * Advanced Deep Learning * Computer Vision --- # 📖 Sebastian Raschka > AI Researcher, Educator, Author of **LLMs From Scratch**. ### 🏷️ Tags `LLMs` • `PyTorch` • `Machine Learning` • `Transformers` ### 🌐 Official Resources | Resource | Link | | --------------- | ------------------------------------------ | | Website | https://sebastianraschka.com | | GitHub | https://github.com/rasbt | | YouTube | https://www.youtube.com/@SebastianRaschka | | Book Repository | https://github.com/rasbt/LLMs-from-scratch | ### 📚 Recommended Content * LLMs From Scratch * Machine Learning Q&A * PyTorch Tutorials * Transformer Architecture * Fine-tuning LLMs ### ⭐ Best For * LLM Engineering * PyTorch * Transformers * Hands-on AI --- # 🎯 Learning Strategy For the best learning experience, follow this order: 1. **Andrew Ng** — Build strong AI and Machine Learning fundamentals. 2. **fast.ai** — Learn practical Deep Learning with real projects. 3. **Andrej Karpathy** — Understand how GPTs and Transformers work internally. 4. **Sebastian Raschka** — Learn to implement and fine-tune LLMs from scratch. 5. **Yann LeCun** — Explore cutting-edge AI research and future directions. --- ## 🚀 Skills You'll Gain After completing these resources, you'll have practical knowledge of: * ✅ Python for AI * ✅ Machine Learning * ✅ Deep Learning * ✅ Neural Networks * ✅ Transformers * ✅ Large Language Models (LLMs) * ✅ Prompt Engineering * ✅ Retrieval-Augmented Generation (RAG) * ✅ AI Agents * ✅ LangChain * ✅ LangGraph * ✅ Model Fine-tuning * ✅ Production AI Applications --- # 📺 YouTube Channels & 📖 Official Documentation Master **Artificial Intelligence**, **Machine Learning**, **Deep Learning**, **Generative AI**, **Large Language Models (LLMs)**, **RAG**, **AI Agents**, **Prompt Engineering**, **LangChain**, **LangGraph**, **MCP**, **Vector Databases**, **MLOps**, and **Production AI Systems** using the best free YouTube channels and official documentation. --- # 📺 Best YouTube Channels for AI Engineers These channels provide free, high-quality tutorials ranging from beginner to advanced. --- # 📊 StatQuest > The best YouTube channel to understand **Machine Learning**, **Statistics**, **Probability**, and **Mathematics** visually. ### 🏷️ Tags `Statistics` • `Machine Learning` • `Probability` • `Data Science` • `Regression` • `Classification` ### 🌐 Channel https://www.youtube.com/@statquest ### 📚 Recommended Playlists * Statistics Fundamentals * Machine Learning * PCA * Decision Trees * Random Forest * XGBoost * Neural Networks * Gradient Descent ### ⭐ Best For * Statistics * Mathematics * Machine Learning Theory --- # 📈 3Blue1Brown > Learn the mathematics behind AI using beautiful visual explanations. ### 🏷️ Tags `Linear Algebra` • `Calculus` • `Neural Networks` • `Vectors` • `Transformers` ### 🌐 Channel https://www.youtube.com/@3blue1brown ### 📚 Recommended Playlists * Essence of Linear Algebra * Essence of Calculus * Neural Networks * Backpropagation * Probability ### ⭐ Best For * AI Mathematics * Deep Learning Foundations --- # 💻 freeCodeCamp > Thousands of hours of free programming, AI, cloud, and software engineering tutorials. ### 🏷️ Tags `Python` • `Machine Learning` • `Deep Learning` • `LangChain` • `Docker` • `Kubernetes` • `FastAPI` ### 🌐 Channel https://www.youtube.com/@freecodecamp ### 📚 Recommended Videos * Python Full Course * Machine Learning Course * Deep Learning with PyTorch * LangChain Course * Docker Course * Kubernetes Course * FastAPI Course * Hugging Face Transformers * LlamaIndex * RAG Tutorials ### ⭐ Best For * End-to-end learning * Hands-on coding * AI projects --- # 🐍 Corey Schafer > One of the best instructors for learning Python and backend development. ### 🏷️ Tags `Python` • `Flask` • `Django` • `Git` • `SQL` ### 🌐 Channel https://www.youtube.com/@coreyms ### 📚 Recommended Playlists * Python Programming * OOP * Virtual Environments * Flask * Django * Git * SQLAlchemy ### ⭐ Best For * Python Fundamentals * Backend Development --- # 🚀 Tech With Tim > Excellent practical tutorials on Python, AI, and software development. ### 🏷️ Tags `Python` • `AI` • `Projects` • `Automation` ### 🌐 Channel https://www.youtube.com/@TechWithTim ### 📚 Recommended Content * Python Projects * AI Projects * Automation * Machine Learning * Programming Tutorials ### ⭐ Best For * Beginners * Project-based Learning --- # 📖 Official Documentation Always prioritize official documentation. It is the most accurate, up-to-date, and production-ready source of information. --- # 🔗 LangChain > The leading framework for building **LLM-powered applications**. ### 🏷️ Tags `Chains` • `Agents` • `Memory` • `Prompt Templates` • `LCEL` • `RAG` ### 🌐 Resources | Resource | Link | | ------------- | ----------------------------------------- | | Documentation | https://python.langchain.com | | API Reference | https://python.langchain.com/docs | | GitHub | https://github.com/langchain-ai/langchain | ### 📚 Learn * Prompt Templates * Chat Models * Output Parsers * Chains * LCEL * Agents * Tools * Memory * Retrieval * RAG * Callbacks * LangSmith Integration --- # 🌐 LangGraph > Build stateful, multi-agent workflows using graph-based orchestration. ### 🏷️ Tags `Agents` • `State Machines` • `Workflow` • `Memory` • `Human-in-the-Loop` ### 🌐 Resources | Resource | Link | | ------------- | ----------------------------------------- | | Documentation | https://langchain-ai.github.io/langgraph | | GitHub | https://github.com/langchain-ai/langgraph | ### 📚 Learn * Graph Nodes * Edges * Conditional Routing * Stateful Agents * Multi-Agent Systems * Memory * Human Approval * Tool Calling --- # 📚 LlamaIndex > Framework for connecting LLMs with private and enterprise data. ### 🏷️ Tags `RAG` • `Embeddings` • `Vector Stores` • `Retrieval` • `Knowledge Base` ### 🌐 Resources | Resource | Link | | ------------- | ---------------------------------------- | | Website | https://www.llamaindex.ai | | Documentation | https://docs.llamaindex.ai | | GitHub | https://github.com/run-llama/llama_index | ### 📚 Learn * Data Connectors * Indexes * Query Engines * Retrieval Pipelines * RAG * Chat Engines * Agents --- # 🦜 Hugging Face Transformers > The most widely used open-source library for working with Transformer models. ### 🏷️ Tags `Transformers` • `BERT` • `GPT` • `Llama` • `Tokenizer` • `Fine-tuning` ### 🌐 Resources | Resource | Link | | ------------- | ------------------------------------------- | | Documentation | https://huggingface.co/docs/transformers | | GitHub | https://github.com/huggingface/transformers | | Models | https://huggingface.co/models | ### 📚 Learn * Pipelines * Tokenizers * AutoModel * AutoTokenizer * Fine-tuning * Inference * Quantization --- # ⚡ PyTorch > The leading deep learning framework used in research and production. ### 🏷️ Tags `Deep Learning` • `Neural Networks` • `CUDA` • `Autograd` ### 🌐 Resources | Resource | Link | | ------------- | ---------------------------------- | | Website | https://pytorch.org | | Tutorials | https://pytorch.org/tutorials | | Documentation | https://pytorch.org/docs | | GitHub | https://github.com/pytorch/pytorch | ### 📚 Learn * Tensor Operations * Autograd * Neural Networks * Data Loaders * Model Training * Distributed Training --- # ☁️ Azure AI Documentation > Official Microsoft documentation for building enterprise AI applications. ### 🏷️ Tags `Azure OpenAI` • `Azure AI Search` • `Azure AI Foundry` • `Semantic Kernel` ### 🌐 Resources | Resource | Link | | ---------------- | --------------------------------------------- | | Azure AI | https://learn.microsoft.com/azure/ai-services | | Azure AI Foundry | https://learn.microsoft.com/azure/ai-foundry | | Semantic Kernel | https://learn.microsoft.com/semantic-kernel | ### 📚 Learn * Azure OpenAI * AI Search * Document Intelligence * Prompt Flow * AI Foundry * Enterprise AI --- # 🤖 OpenAI Documentation > Official guides for building AI applications using OpenAI models and APIs. ### 🏷️ Tags `GPT` • `Responses API` • `Embeddings` • `Agents SDK` • `Function Calling` ### 🌐 Resources | Resource | Link | | ------------- | ----------------------------------------- | | Platform | https://platform.openai.com | | Documentation | https://platform.openai.com/docs | | Cookbook | https://github.com/openai/openai-cookbook | ### 📚 Learn * Responses API * Structured Outputs * Function Calling * Embeddings * Image Generation * Audio APIs * Agents SDK * Fine-tuning --- ## 💡 Documentation Learning Order 1. Python Documentation 2. PyTorch Documentation 3. Hugging Face Transformers 4. OpenAI Documentation 5. LangChain 6. LangGraph 7. LlamaIndex 8. Azure AI Documentation --- ## 🎯 Skills You'll Build By mastering these channels and official documentation, you'll gain expertise in: * ✅ Python Programming * ✅ AI Mathematics * ✅ Machine Learning * ✅ Deep Learning * ✅ Transformer Architecture * ✅ Large Language Models (LLMs) * ✅ Prompt Engineering * ✅ Embeddings & Vector Databases * ✅ Retrieval-Augmented Generation (RAG) * ✅ AI Agents & Multi-Agent Systems * ✅ LangChain & LangGraph * ✅ Enterprise AI Development * ✅ Production AI Systems * ✅ AI Deployment & MLOps --- # 💻 GitHub Repositories Discover the best open-source repositories for **Machine Learning**, **Deep Learning**, **Large Language Models (LLMs)**, **Generative AI**, **RAG**, **AI Agents**, **MLOps**, and **Production AI**. --- # ⭐ Awesome AI Collections ## 🚀 Awesome Machine Learning **Tags** `Machine Learning` • `Deep Learning` • `AI` • `Data Science` **Repository** https://github.com/josephmisiti/awesome-machine-learning --- ## 🤖 Awesome Generative AI **Tags** `Generative AI` • `LLMs` • `GPT` • `Diffusion Models` **Repository** https://github.com/steven2358/awesome-generative-ai --- ## 💡 Awesome AI Apps **Tags** `AI Apps` • `LLM Applications` • `Projects` **Repository** https://github.com/Arindam200/awesome-ai-apps --- # 🧠 LLM Development ## OpenAI Cookbook **Tags** `GPT` • `Embeddings` • `Function Calling` • `Agents SDK` • `RAG` https://github.com/openai/openai-cookbook --- ## Hugging Face Transformers **Tags** `Transformers` • `BERT` • `GPT` • `Llama` https://github.com/huggingface/transformers --- ## LLMs From Scratch **Author:** Sebastian Raschka **Tags** `PyTorch` • `Transformers` • `LLMs` https://github.com/rasbt/LLMs-from-scratch --- ## Ollama **Tags** `Local LLMs` • `Inference` https://github.com/ollama/ollama --- ## vLLM **Tags** `Serving` • `Inference` • `GPU` https://github.com/vllm-project/vllm --- # 🔗 AI Frameworks ## LangChain https://github.com/langchain-ai/langchain --- ## LangGraph https://github.com/langchain-ai/langgraph --- ## LlamaIndex https://github.com/run-llama/llama_index --- # 📚 Books The following books are highly recommended for AI Engineers. | Book | Topics | | --------------------------------------------------------------- | --------------------- | | Python Data Science Handbook | Python, NumPy, Pandas | | Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow | Machine Learning | | Deep Learning (Ian Goodfellow) | Deep Learning | | Practical Deep Learning for Coders | PyTorch | | Natural Language Processing with Transformers | NLP, Transformers | | LLMs From Scratch | LLM Architecture | | Designing Machine Learning Systems | MLOps | | AI Engineering | Production AI | | Building LLM Powered Applications | Enterprise AI | | Generative AI with LLMs | LLM Applications | --- # ✍️ Blogs Follow these blogs to stay updated with the latest advancements. | Blog | Website | | ---------------------- | --------------------------------- | | OpenAI Blog | https://openai.com/news | | Anthropic Blog | https://www.anthropic.com/news | | Google AI Blog | https://blog.google/technology/ai | | Microsoft AI Blog | https://blogs.microsoft.com | | NVIDIA Developer Blog | https://developer.nvidia.com/blog | | Hugging Face Blog | https://huggingface.co/blog | | LangChain Blog | https://blog.langchain.dev | | Sebastian Raschka Blog | https://sebastianraschka.com/blog | | fast.ai Blog | https://www.fast.ai/posts | --- # 📄 Research Papers Every AI Engineer should read these landmark papers. ## Foundation Models * Attention Is All You Need * BERT * GPT * GPT-2 * GPT-3 * GPT-4 Technical Report * InstructGPT --- ## Open Models * LLaMA * LLaMA 2 * LLaMA 3 --- ## Prompt Engineering * Chain of Thought Prompting * Self-Consistency * Tree of Thoughts * ReAct --- ## Tool Use & Agents * Toolformer * Gorilla * Voyager * AutoGPT * SWE-Agent --- ## Retrieval-Augmented Generation (RAG) * Retrieval-Augmented Generation * Self-RAG * Corrective RAG (CRAG) * GraphRAG * RAPTOR * HyDE --- ## Embeddings & Vector Search * Sentence-BERT (SBERT) * Dense Passage Retrieval (DPR) * ColBERT --- # 🤖 Featured Agent Engineering Guides - [🧩 Agentic AI Open-Source Tools Explained](./Agentic-AI-Open-Source-Tools-Explained.md) — colorful, analogy-driven guide to LangChain, LangGraph, LlamaIndex, CrewAI, MCP, A2A, RAG and the modern agentic AI stack. - [Agent: Frameworks & Protocols](./Agent-Frameworks-and-Protocols.md) — beginner-friendly guide to agent frameworks, MCP, A2A, orchestration and interview Q&A. --- # 🎯 Featured AI Engineering Guides - [Agent: Frameworks & Protocols](./Agent-Frameworks-and-Protocols.md) — beginner-friendly guide to agent frameworks, MCP, A2A, orchestration and interview Q&A. - [Embeddings Deep Dive](./Embeddings-Deep-Dive.md) — beginner-to-production guide to embeddings, vector search, RAG, evaluation and interview Q&A. - [Prompt Engineering: Introduction & Security](./Prompt-Engineering-Introduction-and-Security.md) — beginner-to-production guide to prompting, evaluation, prompt injection, RAG security, agent security and interview Q&A. --- # 🗺️ AI Engineer Roadmap ```text Programming │ ├── Python ├── Git & GitHub ├── Linux ├── SQL └── Data Structures ↓ Mathematics │ ├── Linear Algebra ├── Probability ├── Statistics └── Calculus ↓ Data │ ├── NumPy ├── Pandas ├── Data Visualization └── Feature Engineering ↓ Machine Learning │ ├── Supervised Learning ├── Unsupervised Learning ├── Model Evaluation └── Scikit-Learn ↓ Deep Learning │ ├── Neural Networks ├── CNN ├── RNN ├── LSTM ├── Attention └── Transformers ↓ Natural Language Processing │ ├── Tokenization ├── Embeddings ├── Vector Search └── Text Generation ↓ Large Language Models │ ├── GPT ├── Claude ├── Gemini ├── Llama └── Mistral ↓ Generative AI │ ├── Prompt Engineering ├── Fine-tuning ├── Function Calling ├── Structured Outputs └── Multimodal AI ↓ RAG │ ├── Chunking ├── Embeddings ├── Vector Databases ├── Hybrid Search └── Evaluation ↓ AI Agents │ ├── Tool Calling ├── Planning ├── Memory ├── Multi-Agent Systems └── MCP ↓ Frameworks │ ├── LangChain ├── LangGraph ├── LlamaIndex ├── Semantic Kernel └── AutoGen ↓ Deployment │ ├── FastAPI ├── Docker ├── Kubernetes ├── CI/CD └── Monitoring ↓ Cloud AI │ ├── Azure AI ├── AWS Bedrock ├── Vertex AI └── Hugging Face ↓ Production AI Systems ``` <img width="508" height="1965" alt="image" src="https://github.com/user-attachments/assets/b7170f60-8fba-4988-b011-5d3e24f86d57" /> --- # 🤝 Contributing Contributions are welcome! You can contribute by: * 📚 Adding free AI resources * 🎥 Sharing YouTube playlists * 📖 Recommending books * 📝 Improving documentation * 💻 Adding open-source repositories * 🔬 Including research papers * 🚀 Suggesting AI projects * 🐛 Fixing broken links Please open an Issue or submit a Pull Request. --- # ⭐ Support If this repository helped you learn Artificial Intelligence, Machine Learning, Generative AI, or become a better AI Engineer: ⭐ Star this repository 🍴 Fork it 📢 Share it with the community ❤️ Happy Learning! --- ## 🔖 License This repository is licensed under the **MIT License**. Feel free to use, modify, and share it with proper attribution. --- <div align="center"> ### 🚀 Keep Learning • Keep Building • Keep Sharing **Made with ❤️ for the AI Community** ⭐ **If you found this repository useful, don't forget to Star it!** </div> # 💻 GitHub Repositories Discover the best open-source repositories for **Machine Learning**, **Deep Learning**, **Large Language Models (LLMs)**, **Generative AI**, **RAG**, **AI Agents**, **MLOps**, and **Production AI**. --- # ⭐ Awesome AI Collections ## 🚀 Awesome Machine Learning **Tags** `Machine Learning` • `Deep Learning` • `AI` • `Data Science` **Repository** https://github.com/josephmisiti/awesome-machine-learning --- ## 🤖 Awesome Generative AI **Tags** `Generative AI` • `LLMs` • `GPT` • `Diffusion Models` **Repository** https://github.com/steven2358/awesome-generative-ai --- ## 💡 Awesome AI Apps **Tags** `AI Apps` • `LLM Applications` • `Projects` **Repository** https://github.com/Arindam200/awesome-ai-apps --- # 🧠 LLM Development ## OpenAI Cookbook **Tags** `GPT` • `Embeddings` • `Function Calling` • `Agents SDK` • `RAG` https://github.com/openai/openai-cookbook --- ## Hugging Face Transformers **Tags** `Transformers` • `BERT` • `GPT` • `Llama` https://github.com/huggingface/transformers --- ## LLMs From Scratch **Author:** Sebastian Raschka **Tags** `PyTorch` • `Transformers` • `LLMs` https://github.com/rasbt/LLMs-from-scratch --- ## Ollama **Tags** `Local LLMs` • `Inference` https://github.com/ollama/ollama --- ## vLLM **Tags** `Serving` • `Inference` • `GPU` https://github.com/vllm-project/vllm --- # 🔗 AI Frameworks ## LangChain https://github.com/langchain-ai/langchain --- ## LangGraph https://github.com/langchain-ai/langgraph --- ## LlamaIndex https://github.com/run-llama/llama_index --- # 📚 Books The following books are highly recommended for AI Engineers. | Book | Topics | | --------------------------------------------------------------- | --------------------- | | Python Data Science Handbook | Python, NumPy, Pandas | | Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow | Machine Learning | | Deep Learning (Ian Goodfellow) | Deep Learning | | Practical Deep Learning for Coders | PyTorch | | Natural Language Processing with Transformers | NLP, Transformers | | LLMs From Scratch | LLM Architecture | | Designing Machine Learning Systems | MLOps | | AI Engineering | Production AI | | Building LLM Powered Applications | Enterprise AI | | Generative AI with LLMs | LLM Applications | --- # ✍️ Blogs Follow these blogs to stay updated with the latest advancements. | Blog | Website | | ---------------------- | --------------------------------- | | OpenAI Blog | https://openai.com/news | | Anthropic Blog | https://www.anthropic.com/news | | Google AI Blog | https://blog.google/technology/ai | | Microsoft AI Blog | https://blogs.microsoft.com | | NVIDIA Developer Blog | https://developer.nvidia.com/blog | | Hugging Face Blog | https://huggingface.co/blog | | LangChain Blog | https://blog.langchain.dev | | Sebastian Raschka Blog | https://sebastianraschka.com/blog | | fast.ai Blog | https://www.fast.ai/posts | --- # 📄 Research Papers Every AI Engineer should read these landmark papers. ## Foundation Models * Attention Is All You Need * BERT * GPT * GPT-2 * GPT-3 * GPT-4 Technical Report * InstructGPT --- ## Open Models * LLaMA * LLaMA 2 * LLaMA 3 --- ## Prompt Engineering * Chain of Thought Prompting * Self-Consistency * Tree of Thoughts * ReAct --- ## Tool Use & Agents * Toolformer * Gorilla * Voyager * AutoGPT * SWE-Agent --- ## Retrieval-Augmented Generation (RAG) * Retrieval-Augmented Generation * Self-RAG * Corrective RAG (CRAG) * GraphRAG * RAPTOR * HyDE --- ## Embeddings & Vector Search * Sentence-BERT (SBERT) * Dense Passage Retrieval (DPR) * ColBERT --- # 🗺️ AI Engineer Roadmap ```text Programming │ ├── Python ├── Git & GitHub ├── Linux ├── SQL └── Data Structures ↓ Mathematics │ ├── Linear Algebra ├── Probability ├── Statistics └── Calculus ↓ Data │ ├── NumPy ├── Pandas ├── Data Visualization └── Feature Engineering ↓ Machine Learning │ ├── Supervised Learning ├── Unsupervised Learning ├── Model Evaluation └── Scikit-Learn ↓ Deep Learning │ ├── Neural Networks ├── CNN ├── RNN ├── LSTM ├── Attention └── Transformers ↓ Natural Language Processing │ ├── Tokenization ├── Embeddings ├── Vector Search └── Text Generation ↓ Large Language Models │ ├── GPT ├── Claude ├── Gemini ├── Llama └── Mistral ↓ Generative AI │ ├── Prompt Engineering ├── Fine-tuning ├── Function Calling ├── Structured Outputs └── Multimodal AI ↓ RAG │ ├── Chunking ├── Embeddings ├── Vector Databases ├── Hybrid Search └── Evaluation ↓ AI Agents │ ├── Tool Calling ├── Planning ├── Memory ├── Multi-Agent Systems └── MCP ↓ Frameworks │ ├── LangChain ├── LangGraph ├── LlamaIndex ├── Semantic Kernel └── AutoGen ↓ Deployment │ ├── FastAPI ├── Docker ├── Kubernetes ├── CI/CD └── Monitoring ↓ Cloud AI │ ├── Azure AI ├── AWS Bedrock ├── Vertex AI └── Hugging Face ↓ Production AI Systems ``` --- # 🤝 Contributing Contributions are welcome! You can contribute by: * 📚 Adding free AI resources * 🎥 Sharing YouTube playlists * 📖 Recommending books * 📝 Improving documentation * 💻 Adding open-source repositories * 🔬 Including research papers * 🚀 Suggesting AI projects * 🐛 Fixing broken links Please open an Issue or submit a Pull Request. --- # ⭐ Support If this repository helped you learn Artificial Intelligence, Machine Learning, Generative AI, or become a better AI Engineer: ⭐ Star this repository 🍴 Fork it 📢 Share it with the community ❤️ Happy Learning! --- ## 🔖 License This repository is licensed under the **MIT License**. Feel free to use, modify, and share it with proper attribution. --- <div align="center"> ### 🚀 Keep Learning • Keep Building • Keep Sharing **Made with ❤️ for the AI Community** ⭐ **If you found this repository useful, don't forget to Star it!** </div>