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# 🚀 Awesome AI Engineer Resources
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# 🤖 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.
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## 🔥 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>
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