This repository provides a structured curriculum for learning machine learning, curated from Siraj Raval's YouTube series. It targets aspiring ML engineers and researchers seeking a guided path through foundational mathematics, practical applications, cloud platforms, and advanced research topics. The benefit is a clear roadmap to acquire a broad understanding of the ML landscape.

How It Works

The curriculum is organized into weekly modules, progressing from foundational concepts like portfolio design and mathematics (backpropagation, loss functions) to practical applications such as stock price prediction, Kaggle competitions, and API development. It also covers cloud platforms (AWS, Google Cloud, Azure), programming languages for ML (Python, NodeJS), and modern research areas like Neural Arithmetic Logic Units and Quantum Machine Learning.

Quick Start & Requirements

• No specific installation commands are provided, as this is a curriculum outline.
• Prerequisites include access to YouTube for the associated video series and general programming knowledge.
• Links to official quick-start: None provided.

Highlighted Details

• Covers a wide range of topics from foundational math to advanced research.
• Includes practical application examples like stock price prediction and Kaggle competitions.
• Integrates cloud platform knowledge (AWS, Google Cloud, Azure).
• Touches upon modern research areas like AlphaGo Zero and Quantum Machine Learning.

Maintenance & Community

• No information on contributors, sponsorships, or community channels is present in the README.

Licensing & Compatibility

• No license information is provided.

Limitations & Caveats

This repository serves as a curriculum outline and does not contain code implementations or direct links to resources beyond the YouTube series. The effectiveness relies heavily on external content and self-directed learning.