Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning
models. This book utilizes a problem-solution approach to explaining machine learning models
and their algorithms. The book starts with model interpretation for supervised learning linear
models which includes feature importance partial dependency analysis and influential data
point analysis for both classification and regression models. Next it explains supervised
learning using non-linear models and state-of-the-art frameworks such as SHAP values scores and
LIME for local interpretation. Explainability for time series models is covered using LIME and
SHAP as are natural language processing-related tasks such as text classification and
sentiment analysis with ELI5 and ALIBI. The book concludes with complex model classification
and regression-like neural networks and deep learning models using the CAPTUM framework that
shows feature attribution neuron attribution and activation attribution. After reading this
book you will understand AI and machine learning models and be able to put that knowledge into
practice to bring more accuracy and transparency to your analyses. What You Will Learn Create
code snippets and explain machine learning models using Python Leverage deep learning models
using the latest code with agile implementations Build train and explain neural network
models designed to scale Understand the different variants of neural network models Who This
Book Is For AI engineers data scientists and software developers interested in XAI