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    Python Feature Engineering Cookbook. A complete guide to crafting powerful features for your machine learning models - Third Edition

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Soledad Galli, Christoph Molnar
    • Niedostępna
    Python Feature Engineering Cookbook. A complete guide to crafting powerful features for your machine learning models - Third Edition Soledad Galli, Christoph Molnar - okladka książki

    Python Feature Engineering Cookbook. A complete guide to crafting powerful features for your machine learning models - Third Edition Soledad Galli, Christoph Molnar - okladka książki

    Python Feature Engineering Cookbook. A complete guide to crafting powerful features for your machine learning models - Third Edition Soledad Galli, Christoph Molnar - audiobook MP3

    Python Feature Engineering Cookbook. A complete guide to crafting powerful features for your machine learning models - Third Edition Soledad Galli, Christoph Molnar - audiobook CD

    Serie wydawnicze:
    Cookbook
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    396
    Dostępne formaty:
         PDF
         ePub
    Streamline data preprocessing and feature engineering in your machine learning project with this third edition of the Python Feature Engineering Cookbook to make your data preparation more efficient.
    This guide addresses common challenges, such as imputing missing values and encoding categorical variables using practical solutions and open source Python libraries.
    You’ll learn advanced techniques for transforming numerical variables, discretizing variables, and dealing with outliers. Each chapter offers step-by-step instructions and real-world examples, helping you understand when and how to apply various transformations for well-prepared data.
    The book explores feature extraction from complex data types such as dates, times, and text. You’ll see how to create new features through mathematical operations and decision trees and use advanced tools like Featuretools and tsfresh to extract features from relational data and time series.
    By the end, you’ll be ready to build reproducible feature engineering pipelines that can be easily deployed into production, optimizing data preprocessing workflows and enhancing machine learning model performance.

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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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