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Paperback or Softback. Etat : New. Python Feature Engineering Cookbook - Second Edition: Over 70 recipes for creating, engineering, and transforming features to build machine learning m 1.46. Book. N° de réf. du vendeur BBS-9781804611302
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Etat : New. N° de réf. du vendeur I-9781804611302
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Vendeur : PBShop.store US, Wood Dale, IL, Etats-Unis
PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9781804611302
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Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
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Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Etat : New. In. N° de réf. du vendeur ria9781804611302_new
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Vendeur : Russell Books, Victoria, BC, Canada
paperback. Etat : New. 2nd ed. Special order direct from the distributor. N° de réf. du vendeur ING9781804611302
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Vendeur : Majestic Books, Hounslow, Royaume-Uni
Etat : New. Print on Demand pp. 386. N° de réf. du vendeur 402226459
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Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python librariesKey Features:Learn and implement feature engineering best practicesReinforce your learning with the help of multiple hands-on recipesBuild end-to-end feature engineering pipelines that are performant and reproducibleBook Description:Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes.This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner.By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.What You Will Learn:Impute missing data using various univariate and multivariate methodsEncode categorical variables with one-hot, ordinal, and count encodingHandle highly cardinal categorical variablesTransform, discretize, and scale your variablesCreate variables from date and time with pandas and Feature-engineCombine variables into new featuresExtract features from text as well as from transactional data with FeaturetoolsCreate features from time series data with tsfreshWho this book is for:This book is for machine learning and data science students and professionals, as well as software engineers working on machine learning model deployment, who want to learn more about how to transform their data and create new features to train machine learning models in a better way. N° de réf. du vendeur 9781804611302
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