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From the back cover: Causal AI teaches you how to build machine learning and deep learning models that implement causal reasoning. Discover why leading AI engineers are so excited by causal reasoning, and develop a high-level understanding of this next major trend in AI. New techniques are demonstrated with example models for solving industry-relevant problems. You'll learn about causality for recommendations; causal modeling of online conversions; and uplift, attribution, and churn modeling. Each technique is tested against a common set of problems, data, and Python libraries, so you can…mehr

Produktbeschreibung
From the back cover: Causal AI teaches you how to build machine learning and deep learning models that implement causal reasoning. Discover why leading AI engineers are so excited by causal reasoning, and develop a high-level understanding of this next major trend in AI. New techniques are demonstrated with example models for solving industry-relevant problems. You'll learn about causality for recommendations; causal modeling of online conversions; and uplift, attribution, and churn modeling. Each technique is tested against a common set of problems, data, and Python libraries, so you can compare and contrast which will work best for you. About the reader: For data scientists and machine learning engineers. A familiarity with probability and statistics will be helpful, but not essential, to begin this guide. Examples in Python.
Autorenporträt
Robert Osazuwa Ness is a leading researcher in causal AI at Microsoft Research. He is a contributor to open-source causal inference packages such as Python’s DoWhy and R’s bnlearn.