• Title: An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI
• Author(s) Patrick Hall and Navdeep Gill
• Publisher: O'Reilly Media, Inc.; eBook (Compliments of H2O.ai and Dataiku)
• Permission: Free eBook is Complimented by H2O.ai and Dataiku
• Paperback: N/A
• eBook: PDF
• Language: English
• ISBN-10/ASIN: N/A
• ISBN-13: 978-1098115456
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Book Description

Understanding and trusting models and their results is a hallmark of good science. Get an applied perspective on how this applies to machine learning, including fairness, accountability, transparency, and explainable AI.

• Patrick Hall is senior director for data science products at H2O.ai. Navdeep Gill is a senior data scientist and software engineer at H2O.ai.

• Amazon
• Amazon (Interpretable Machine Learning with Python)

• Machine Learning
• Deep Learning and Neural Networks
• Artificial Intelligence
• Statistics, Mathematical Statistics, and SAS Programming
• Probability and Stochastic Processes

• An Introduction to Machine Learning Interpretability (Patrick Hall, et al.)
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• Book Homepage (Dataiku)

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