Part I - Foundations

Part I of "Machine Learning via Rust (MLVR)" lays a critical foundation for readers by bridging the gap between the fundamental concepts of machine learning and the unique capabilities of the Rust programming language. This section starts with an exploration of the core challenges and objectives in machine learning, setting the stage for a deep understanding of the field's principles. It then transitions into a comprehensive introduction to Rust, a language renowned for its performance, memory safety, and concurrency, which makes it particularly well-suited for machine learning applications. Readers are guided through essential mathematical concepts that underpin machine learning, such as linear algebra, probability, and optimization, ensuring they have the necessary tools to succeed. Finally, the section concludes with an in-depth exploration of the Rust ecosystem, focusing on key libraries and crates that enable efficient and scalable machine learning implementations. By the end of Part I, readers will have a solid grounding in both the theoretical aspects of machine learning and the practical skills needed to leverage Rust for their projects.