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    Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models - Second Edition

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autor:
    Soledad Galli
    Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models - Second Edition Soledad Galli - okladka książki

    Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models - Second Edition Soledad Galli - okladka książki

    Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models - Second Edition Soledad Galli - audiobook MP3

    Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models - Second Edition Soledad Galli - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    386
    Dostępne formaty:
         PDF
         ePub
    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.

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    O autorze książki

    Soledad Galli is a lead data scientist with more than 10 years of experience in world-class academic institutions and renowned businesses. She has researched, developed, and put into production machine learning models for insurance claims, credit risk assessment, and fraud prevention. Soledad received a Data Science Leaders' award in 2018 and was named one of LinkedIn's voices in data science and analytics in 2019. She is passionate about enabling people to step into and excel in data science, which is why she mentors data scientists and speaks at data science meetings regularly. She also teaches online courses on machine learning in a prestigious Massive Open Online Course platform, which have reached more than 10,000 students worldwide.

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