This self-paced bootcamp provides practical, real-world applications for generative AI, targeting aspiring AI engineers. It aims to equip learners with the skills to build and deploy AI solutions, covering foundational ML, LLMs, RAG, and AI agents.

How It Works

The bootcamp is structured around video tutorials and written guides covering key areas of generative AI. It emphasizes practical implementation, guiding users through building projects like RAG systems, fine-tuning LLMs (Llama 3), and developing AI agents using frameworks like CrewAI. The approach focuses on actionable steps for real-world deployment.

Quick Start & Requirements

• Install: No specific installation command is provided; content is primarily tutorial-based.
• Prerequisites: Python, PyTorch, familiarity with ML fundamentals. Specific project requirements (e.g., API keys for Groq, specific LLM versions) may vary per tutorial.
• Resources: Links to YouTube videos and written tutorials are provided.

Highlighted Details

• Covers foundational ML skills, including data analysis, model development, and evaluation.
• Deep dives into LLMs, prompt engineering, RAG, and LLM deployment.
• Features AI agent development with frameworks like CrewAI and tools like Llama 3 and GPT-4 Turbo.
• Includes practical projects such as building a private document chat system (RagBase) and social media content generation.

Maintenance & Community

• The project is maintained by curiousily.
• Links to YouTube are provided for video content. No other community or roadmap links are present.

Licensing & Compatibility

• The README does not specify a license.

Limitations & Caveats

The bootcamp is presented as a collection of tutorials and projects; it does not appear to be a cohesive, runnable codebase with a single entry point. Specific project dependencies and setup instructions are likely detailed within individual tutorials.