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120671597
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
Item #:
120671597
STD 1680546
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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.
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- 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:Pick the right machine learning approach for your dataApply deep learning to tabular dataDeploy tabular machine learning locally and in the cloudPipelines to automatically train and maintain a modelMachine Learning for Tabular Data covers classic machine learning techniques like gradient boosting, and more contemporary deep learning approaches. By the time you’re finished, you’ll be equipped with the skills to apply machine learning to the kinds of data you work with every day.Foreword by Antonio Gulli.About the technologyMachine 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.About the bookMachine 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.What's insideMaster XGBoostApply deep learning to tabular dataDeploy models locally and in the cloudBuild pipelines to train and maintain modelsAbout the readerFor readers experienced with Python and the basics of machine learning.About the authorMark Ryan is the AI Lead of the Developer Knowledge Platform at Google. A three-time Kaggle Grandmaster, Luca Massaron is a Google Developer Expert (GDE) in machine learning and AI. He has published 17 other books.Table of ContentsPart 11 Understanding tabular data2 Exploring tabular datasets3 Machine learning vs. deep learningPart 24 Classical algorithms for tabular data5 Decision trees and gradient boosting6 Advanced feature processing methods7 An end-to-end example using XGBoostPart 38 Getting started with deep learning with tabular data9 Deep learning best practices10 Model deployment11 Building a machine learning pipeline12 Blending gradient boosting and deep learningA Hyperparameters for classical machine learning modelsB K-nearest neighbors and support vector machines
| Publisher | Manning |
| Publication date | March 25, 2025 |
| Language | English |
| Print length | 504 pages |
| ISBN-10 | 1633438546 |
| ISBN-13 | 978-1633438545 |
| Item Weight | 1.87 pounds (850 grams) |
| Dimensions | 7.38 x 1 x 9.25 inches (18.7 x 2.5 x 23.5 cm) |
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STD 1680546
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Features & Benefits
- Learn to apply machine learning to everyday tabular business data.
- Master advanced techniques like XGBoost and LightGBM.
- Optimize deep learning libraries for effective model training.
- Deploy machine learning models locally or in the cloud.
- Create automated pipelines for model training and maintenance.
- Upgrade your data analysis pipeline with practical ML techniques.
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