This repository aggregates popular cheatsheets and quick reference guides for Artificial Intelligence (AI) and Machine Learning (ML). It serves as a centralized resource for students, researchers, and practitioners seeking concise summaries of core concepts, algorithms, and tools across the AI/ML landscape.

How It Works

The repository is organized into folders mirroring a typical AI/ML learning roadmap, covering foundational mathematics, programming languages (Python, C++), computer architecture, data structures, and various AI/ML subfields like deep learning, reinforcement learning, and MLOps. Each folder contains multiple cheatsheets, often presented in PDF format, offering distilled information on specific topics.

Quick Start & Requirements

• Access files directly via GitHub or download from the provided Google Drive link for easier browsing.
• No specific software installation is required to view the cheatsheets.

Highlighted Details

• Comprehensive coverage from foundational math to advanced deep learning and MLOps.
• Includes practical guides on data cleaning, visualization, and feature engineering.
• Offers multiple cheatsheets for key libraries like NumPy, Pandas, and Scikit-Learn.
• Features comparative analyses of ML algorithms and their time complexities.

Maintenance & Community

• Maintained by SamBelkacem.
• No community links (Discord, Slack) or roadmap are provided in the README.

Licensing & Compatibility

• The repository does not explicitly state a license.
• Compatibility for commercial use or closed-source linking is undetermined.

Limitations & Caveats

The lack of an explicit license makes commercial use or integration into proprietary systems uncertain. The repository is a curated collection of external resources, and the freshness or accuracy of individual cheatsheets is not guaranteed.