{"product_id":"9781633438545","title":"Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eRyan, Mark; Massaron, Luca\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e3\/25\/2025\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003eBusiness runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003ci\u003eMachine Learning for Tabular Data\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003e\u003ci\u003eMachine Learning for Tabular Data\u003c\/i\u003e will teach you how to:\u003cul\u003e\n\u003cli\u003ePick the right machine learning approach for your data\u003c\/li\u003e\n\u003cli\u003eApply deep learning to tabular data\u003c\/li\u003e\n\u003cli\u003eDeploy tabular machine learning locally and in the cloud\u003c\/li\u003e\n\u003cli\u003ePipelines to automatically train and maintain a model\u003c\/li\u003e\n\u003c\/ul\u003e\u003ci\u003eMachine Learning for Tabular Data\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003eForeword by \u003cb\u003eAntonio Gulli\u003c\/b\u003e.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eMachine 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003ci\u003eMachine Learning for Tabular Data\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eWhat's inside\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eMaster XGBoost\u003c\/li\u003e\n\u003cli\u003eApply deep learning to tabular data\u003c\/li\u003e\n\u003cli\u003eDeploy models locally and in the cloud\u003c\/li\u003e\n\u003cli\u003eBuild pipelines to train and maintain models\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eFor readers experienced with Python and the basics of machine learning.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eMark Ryan\u003c\/b\u003e is the AI Lead of the Developer Knowledge Platform at Google. A three-time Kaggle Grandmaster, \u003cb\u003eLuca Massaron\u003c\/b\u003e is a Google Developer Expert (GDE) in machine learning and AI. He has published 17 other books.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003ePart 1\u003cbr\u003e1 Understanding tabular data\u003cbr\u003e2 Exploring tabular datasets\u003cbr\u003e3 Machine learning vs. deep learning\u003cbr\u003ePart 2\u003cbr\u003e4 Classical algorithms for tabular data\u003cbr\u003e5 Decision trees and gradient boosting\u003cbr\u003e6 Advanced feature processing methods\u003cbr\u003e7 An end-to-end example using XGBoost\u003cbr\u003ePart 3\u003cbr\u003e8 Getting started with deep learning with tabular data\u003cbr\u003e9 Deep learning best practices\u003cbr\u003e10 Model deployment\u003cbr\u003e11 Building a machine learning pipeline\u003cbr\u003e12 Blending gradient boosting and deep learning\u003cbr\u003eA Hyperparameters for classical machine learning models\u003cbr\u003eB K-nearest neighbors and support vector machines","brand":"Manning","offers":[{"title":"Default Title","offer_id":45629964386559,"sku":"9781633438545","price":59.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_6b50cf4c-c716-4d01-932e-6827fe763709.jpg?v=1771351390","url":"https:\/\/massivebookshop.com\/products\/9781633438545","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}