This tutorial will introduce neural operators, an extension of neural networks designed to learn mappings between infinite-dimensional function spaces. We'll cover the theoretical foundations, including their formulation and universal approximation capabilities. Emphasizing their discretization-invariance, we'll explore how neural operators tackle problems in partial differential equations (PDEs) and scientific computing tasks. This session is ideal for machine learning experts looking to leverage neural operators for advanced scientific and engineering applications.