This repository provides code examples and tutorials for various machine learning topics, targeting individuals passionate about learning and exploring ML concepts. It serves as a practical resource for understanding and implementing techniques in computer vision, NLP, MLOps, and LLMs using popular Python libraries.

How It Works

The project is structured around Python, leveraging core libraries like NumPy and Pandas for data manipulation, Matplotlib for visualization, and PyTorch/TensorFlow for deep learning. It aims to demystify complex ML concepts through practical code implementations, making it easier for users to follow along with Twitter-based tutorials.

Quick Start & Requirements

• Primary install: pip install -r requirements.txt
• Prerequisites: Python installed on your system.
• Official Docs: CONTRIBUTING.md
• Connect: Twitter @akshay_pachaar

Highlighted Details

• Covers a broad spectrum of ML topics from foundational libraries to advanced LLMs.
• Focuses on practical implementation alongside Twitter-based tutorials.
• Includes MLOps and deep learning frameworks like PyTorch and TensorFlow.

Maintenance & Community

The project is maintained by Akshay Pachaar, who shares updates and tutorials on Twitter. Contribution guidelines are available in CONTRIBUTING.md.

Licensing & Compatibility

Licensed under the MIT License, allowing for broad use and modification, including commercial applications.

Limitations & Caveats

The repository is primarily a collection of code examples tied to specific Twitter tutorials, and may not represent a fully integrated or production-ready ML framework. Users should refer to individual tutorial contexts for specific usage details.