Not sure which evaluation metric you should choose for your binary classification problem? You want to know for each metric, the definition and intuition behind it, the non-technical explanation that you can communicate to business stakeholders, how to calculate or plot it, and when you should use it. You will learn about a bunch of common and lesser-known evaluation metrics and charts to understand how to choose the model performance metric for your problem. After reading this blog post you should have a good idea.
This article explains what experiment management is, and how organizing your model development process improves your workflow. Adding experiment management tools to standard software development best practices can make machine learning projects more likely to succeed. You will learn about tracking ML experiments, code version control for data science, tracking hyperparameters. You will also learn about data versioning, tracking machine learning metrics, experiment organization, working in creative iteration, and model results exploration.