Mastering Design Principles for Machine Learning

Software design principles are general guides for developing clean, readable and maintainable code. Design principles are important because they provide best practices that help ensure that code can be easily understood, reused, scaled and tested. Writing code without incorporating at least some of the known best practices can lead to code that is hard to understand. If code is hard to read that makes it more difficult to explain, modify, and maintain. There are many design principles used for software development. Generally, these principles can be place into three buckets: Clarity, maintainability and collaboration. In the clarity bucket important concepts include readability, Keep it Simple Stupid (KISS), Don’t Repeat Yourself (DRY) and modularity. In the maintenance bucket we have single responsibility principle (SRP), testability, and error handling. In the collaboration bucket we have Version Control and Documentation. Obviously, these categories have significant overlap. For example, modular code usually adheres to SRP. Despite this, it helps to consider each of these concepts independently. <h2>Clarity</h2> There are many ways to improve code readability, particularly in python. These include descriptive naming, consistent indentation, breaking down complex tasks, minimizing long lines of code, grouping related code, and more. Modularity also helps with code readability and clarity. Modularity involves grouping similar code logic using modules, classes, methods and functions. This also makes code easy to maintain, reuse and share with other developers. Lack of modularity in code can lead to tightly couple functionality which can become very complex. This can make adding new features or debugging difficult or even impossible. <a href="https://medium.datadriveninvestor.com/mastering-design-principles-for-machine-learning-20a9b8de5432">Visit Now</a>