Advanced Julia explores the language’s more sophisticated features to optimize performance and build complex applications. Topics include metaprogramming, which allows code generation and macros; multiple dispatch for designing flexible and efficient functions; and parallel and distributed computing for handling large-scale computations. It also covers performance optimization techniques, such as type declarations, memory management, and profiling. Advanced Julia includes working with external libraries, creating packages, and integrating Julia with other languages like Python, C, and R. Mastery of these concepts enables developers and researchers to build high-performance, scalable solutions for scientific computing, data analysis, and machine learning.