Book Image

Interpretable Machine Learning with Python - Second Edition

By : Serg Masís
4 (4)
Book Image

Interpretable Machine Learning with Python - Second Edition

4 (4)
By: Serg Masís

Overview of this book

Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps. In addition to the step-by-step code, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. By the end of the book, you’ll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.
Table of Contents (17 chapters)
15
Other Books You May Enjoy
16
Index

Who this book is for

This book caters to a diverse audience, including:

  • Data professionals who face the growing challenge of explaining the functioning of AI systems they create and manage and seek ways to enhance them.
  • Data scientists and machine learning professionals aiming to broaden their expertise by learning model interpretation techniques and strategies to overcome model challenges from fairness to robustness.
  • Aspiring data scientists who have a basic grasp of machine learning and proficiency in Python.
  • AI ethics officers aiming to deepen their knowledge of the practical aspects of their role to guide their initiatives more effectively.
  • AI project supervisors and business leaders eager to integrate interpretable machine learning in their operations, aligning with the values of fairness, responsibility, and transparency.