AI Bootcamp

Report Abuse

Basic Information

AI-Bootcamp is a self-paced educational repository designed to teach generative AI concepts and practical techniques. The materials focus on foundational machine learning concepts and move into modern large language model topics such as LLMs, retrieval-augmented generation (RAG), and agent systems. The curriculum explicitly calls out tooling and frameworks relevant to building applications, listing LangChain and LangGraph as subjects and mentioning fine-tuning of Llama 3. The repository aims to provide a coherent learning path for practitioners who want to understand both theory and applied workflows for creating and extending generative AI systems. It is presented as a structured bootcamp with tutorial-style content for learners to follow at their own pace.

Links

App Details

Features
The repository features a collection of tutorials and a structured curriculum covering ML fundamentals, LLM concepts, RAG methodologies, and agent architectures. It highlights hands-on topics such as LangChain and LangGraph integration and fine-tuning models like Llama 3. As a bootcamp resource, it is organized for self-paced study and likely groups lessons or modules to progress from basics to advanced topics. The materials emphasize practical skills for building and experimenting with generative models and AI agents. The README and repo signals prioritize learning pathways and tooling exposure rather than providing a single turnkey application.
Use Cases
This bootcamp helps learners and developers acquire practical knowledge for building generative AI systems and agents. It consolidates essential topics—machine learning fundamentals, working with LLMs, implementing RAG pipelines, using agent frameworks, and fine-tuning modern models—into a single learning resource to reduce the effort of finding disparate tutorials. By presenting focused tutorials on LangChain, LangGraph, and Llama 3 fine-tuning, it prepares readers to prototype and iterate on applications that use retrieval and agent orchestration. The self-paced format lets users learn incrementally and apply new techniques directly to projects or experiments.

Please fill the required fields*