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Textbook in PDF format

Most intermediate-level Machine Learning books usually focus only on how to optimize models by increasing accuracy or decreasing prediction error. However, this focus often overlooks the importance and the need to be able to explain the "why" and "how" of why your ML model makes the predictions it does.
This book brings together the best in class techniques for model interpretability and explaining model predictions in a hands-on approach so that experienced ML practitioners can more easily apply these tools in their daily workflow.
A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needs
Tips and best practices for implementing these techniques
A guide to interacting with explainability and how to avoid common pitfalls
The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems
Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data
Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace