Mark Ryan and Luca Massaron
Foreword by Antonio Gulli
MEAP began August 2023 Publication in February 2025
ISBN 9781633438545 504 pages printed in black & white
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Running code directly on Google Colab: 
Chapter 2
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Appendix B |
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The book is available here: http://mng.bz/KeP0
!!! To celebrate the release of your book, we would like to offer you a launch discount code PBryan2, valid through February 19, which offers your network and contacts 45% off your book in all formats !!!
Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques.
Machine Learning for Tabular Data teaches you to train insightful machine learning models on common tabular business data sources such as spreadsheets, databases, and logs. You’ll discover how to use XGBoost and LightGBM on tabular data, optimize deep learning libraries like TensorFlow and PyTorch for tabular data, and use cloud tools like Vertex AI to create an automated MLOps pipeline.
Machine Learning for Tabular Data will teach you how to:
- Master XGBoost
- Apply deep learning to tabular data
- Deploy models locally and in the cloud
- Build pipelines to train and maintain your models
About the book
Machine learning can accelerate everyday business chores like account reconciliation, demand forecasting, and customer service automation—not to mention more exotic challenges like fraud detection, predictive maintenance, and personalized marketing. This book shows you how to unlock the vital information stored in spreadsheets, ledgers, databases, and other tabular data sources using gradient boosting, deep learning, and generative AI.
Machine Learning for Tabular Data delivers practical ML techniques to upgrade every stage of the business data analysis pipeline. In it, you’ll explore examples like using XGBoost and Keras to predict short-term rental prices, deploying a local ML model with Python and Flask, and streamlining workflows using large language models (LLMs). Along the way, you’ll learn to make your models both more powerful and more explainable
From the cover
“Explores the exciting potential of generative AI in tabular data analysis, showcasing its applications in synthetic data generation, feature engineering, and model interpretation. —Gus Martins, Google”
“An invaluable, hands-on resource to learn practical machine learning techniques without getting lost in overly complex theory. —Dmitry Efi mov, Amazon ”
“Equips you with the knowledge to tackle any tabular data problem. Luca and Ryan have done a great job covering this rich field. —Bojan Tunguz, Tabul.AI ”
“Helps you unlock the full
potential of machine learning
for tabular data, enabling you
to choose the best approach for
your scenario.
—Shotaro Ishihara, Nikkei Inc.”
About the reader
For readers experienced with Python and the basics of machine learning.
About the authors
Mark Ryan is the AI Lead of the Developer Knowledge Platform at Google.
Luca Massaron is a 3-time Kaggle Grandmaster and a Google Developer Expert (GDE) in machine learning and AI. He has published 18 books so far.