Explainable AI for Practitioners

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.