🤖 AI/ML Codex

A collection of my curated AI & Machine Learning projects — building the future and making an impact!

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🌟 Vision

This repository serves as my living codex of AI/ML explorations, experiments, and full-fledged projects.
From research prototypes to production-grade systems, the goal is simple:
to push the boundaries of what’s possible with AI, learn relentlessly, and share that journey.

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📂 Contents

Here’s what you’ll find inside this codex:

• 📈 Applied AI Projects

  • LLM-powered apps (RAG, autonomous agents, assistants)
  • AI-first full-stack systems (finance, productivity, healthcare, education)

• 🧠 Core ML Projects

  • Classical ML algorithms (SVMs, Random Forests, Regression)
  • Deep Learning models (CNNs, RNNs, Transformers)

• ⚙️ MLOps & Deployment

  • Experiment tracking
  • Model serving
  • CI/CD for AI

• 🧪 Research & Experiments

  • Novel architectures & training approaches
  • Explorations with embeddings, multi-agent systems, and reinforcement learning

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🛠️ Tech Stack

Some of the tools & frameworks you’ll (soon) see across projects:

• Languages: Python
• ML/AI: PyTorch, Transformers (Hugging Face), scikit-learn, NumPy, Pandas
• LLMs & Agents: LangChain, AutoGen, LlamaIndex, CrewAI, Gemini, Google ADK,
• Visualisation: Matplotlib, Plotly, Streamlit

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🚀 Getting Started

Clone the repository and explore any project:

git clone https://github.com/your-username/ai-ml-codex.git
cd ai-ml-codex/project-folder

📜 Roadmap

• Expand Codex with 10+ production-ready AI apps
• Add detailed tutorials & blog posts

⚠️ Disclaimer

These projects are for learning, research, and demonstration purposes. They are not intended to provide financial, medical, or legal advice. Use responsibly.

🤝 Contributions

This codex is personal yet open — feel free to fork, star ⭐, or open issues for ideas, feedback, or improvements.