Visualizing Machine Learning Models with Plotly

Evaluating and explaining machine learning models can be difficult when you are only looking at static tables and raw numbers. This text-based course teaches you how to transform complex model outputs into clear, interactive Plotly visualizations. You will learn to diagnose model performance, communicate insights effectively, and make your data science workflows more transparent. What you'll learn: - Understand foundational visualization concepts and the core Plotly Express API - Build interactive classification plots, including decision boundaries and confusion matrices - Visualize regression outputs, residual plots, and prediction intervals - Generate modern model evaluation curves like ROC and Precision-Recall interactively - Map feature importance and basic model interpretability metrics clearly - Apply clean-code practices to structure reusable visualization pipelines Starting with key terminology and basic plotting setups, you will progress through structured text explanations and clear code snippets that demonstrate how to visualize both classification and regression models. This course is perfect for aspiring data scientists and developers looking to improve their model evaluation workflow. No prior visualization experience is required. Begin reading now to master interactive machine learning visualization.