Writing functional Python code for machine learning tasks is a primary objective. However, as projects scale and involve collaboration, the quality of that code becomes equally important. Code that is difficult to read, slow to execute, or hard to modify can significantly hinder progress.

This chapter concentrates on the practices and tools that help you write Python code for machine learning that is not just correct, but also efficient, readable, and maintainable. We will cover establishing clear code style, structuring your projects logically, and writing effective functions and modules. You'll learn about managing project dependencies with virtual environments, identifying performance bottlenecks using profiling, and specific techniques for optimizing common libraries like NumPy and Pandas. Furthermore, we will introduce the fundamentals of unit testing for verifying code components and the basics of version control using Git to manage your codebase effectively. These skills are essential for building reliable and scalable machine learning systems.