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Basic Information

This repository is a curated, topic-organized collection of free resources to learn AI engineering, machine learning, large language models, generative AI and agents. It serves as a learning roadmap that aggregates links to courses, videos, tutorials, books, papers and tooling across foundational mathematics, Python, ML fundamentals, deep learning, computer vision, NLP, reinforcement learning, generative models and LLM-specific topics. The README organizes materials into sections such as mathematical foundations, frameworks and libraries, deep learning specializations, LLMs and APIs, prompt engineering, retrieval-augmented generation, AI agents, model context protocol, MLOps and deployment, guides, books and must-read papers. The target audience is self-learners and practitioners who want a central index of reputable free and open resources to design a study plan or find references for specific subtopics in AI engineering.

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Features
The README provides a structured catalogue of resources grouped by topic and learning stage. It includes roadmaps and curated course links from platforms like Coursera, DeepLearning.AI, Hugging Face and university offerings. It lists common frameworks and libraries such as scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch and Keras. LLM-focused sections highlight conceptual explainers, open source models, APIs and tools including LangChain, LlamaIndex and Ollama. There are sections on prompt engineering, RAG, AI agents and the Model Context Protocol, plus MLOps and deployment tools like Streamlit and MLflow. The README also links to books, YouTube channels, papers with code, Kaggle and a curated list of must-read foundational AI papers.
Use Cases
This repository helps learners quickly discover vetted educational materials across the breadth of AI engineering topics without searching multiple sites. It provides a guided taxonomy so users can progress from mathematical foundations and Python to ML fundamentals, deep learning specializations and applied topics like MLOps, LLM tooling and agent development. By collecting courses, tutorials, frameworks, APIs, books, videos and canonical papers in one place, it reduces onboarding time, supports curriculum planning and supplies reference links for practical implementation and research reading. The resource is useful for beginners building a study path and intermediate practitioners seeking targeted materials on LLMs, prompt engineering, RAG and deployment.

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