I find the activity of codifying and comparing various interpretations of a particular process in the pursuit of strengthening one's own interpretation of said process to be a worthy one. I have previously done so with alternate interpretations of what we could call the machine learning process (and which could reasonably be closely aligned with the data science or data mining processes, at least to some degree), of which you can find examples here and here and here. These previous posts have considered the classic CRISP-DM model, the KDD Process, Francois Chollet's 4 step model (aimed at Keras, but generalizable), Yufeng Guo's 7 steps to machine learning, and even modifications aimed specifically at more narrow disciplines, such as the text-based data science task framework. In an effort to further refine our internal models, this post will present an overview of Aurélien Géron's Machine Learning Project Checklist, as seen in his bestselling book, "Hands-On Machine Learning with Scikit-Learn & TensorFlow." It's a similar approach to that of, say, Guo's 7 step process, but at a subtly higher level; it's presented as a checklist of approaching projects, and so it feels less prescriptive and more descriptive, a reminder of what you should be doing as you do it as opposed to some grand explanation of why you are doing what you are doing.