Applied machine learning scikit learn di kuester max (5 risultati)

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  • Lingua: Inglese

    Editore: Amazon Digital Services LLC - Kdp, 2025

    9798275717419

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    Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US

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    Condizione: Nuovo

    EUR 43,22

     Spedizione gratuita 
    Spedito in U.S.A.

    Quantità: Più di 20 disponibili

    PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

  • Lingua: Inglese

    Editore: Amazon Digital Services LLC - Kdp, 2025

    9798275717419

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    Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK

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    Condizione: Nuovo

    EUR 38,53

    EUR 5,86 spedizione 
    Spedito da Regno Unito a U.S.A.

    Quantità: Più di 20 disponibili

    PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

  • Lingua: Inglese

    Editore: Independently published, 2025

    9798275717419

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    • Print on Demand

    Da: California Books, Miami, FL, U.S.A.California Books

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    Condizione: Nuovo

    EUR 36,82

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    Quantità: Più di 20 disponibili

    Condizione: New. Print on Demand.

  • Lingua: Inglese

    Editore: Independently Published, 2025

    9798275717419

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    • Print on Demand

    Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail

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    Condizione: Nuovo

    EUR 42,26

     Spedizione gratuita 
    Spedito in U.S.A.

    Quantità: 1 disponibili

    Paperback. Condizione: new. Paperback. What if you could understand machine-learning results with complete confidence-without getting lost in complicated math or confusing explanations?This book gives you that clarity.BOOK TITLES delivers a practical, beginner-friendly path to mastering real-world machine learning using Scikit-Learn, the most trusted toolkit in Python. At its core, this book solves a single problem: helping you move from "I understand the idea" to "I can actually build and evaluate models that work." Every chapter builds skill, accuracy, and confidence-without overwhelming theory.You'll quickly learn how to structure data, choose the right algorithm, train models efficiently, and evaluate them with meaningful metrics. Through clear explanations and hands-on examples, you'll understand why models behave the way they do and how to improve them with smarter preprocessing, tuning, and validation techniques.You'll be able to: - Build classification, regression, and clustering models that produce reliable results.- Apply essential preprocessing steps such as scaling, encoding, and feature selection.- Evaluate models using precision, recall, F1-score, confusion matrices, and cross-validation.- Strengthen your models through hyperparameter tuning, pipelines, and proper train/test practices.- Work effectively with real datasets and interpret outcomes with confidence.- Understand advanced topics such as ensemble methods, dimensionality reduction, and model optimization-explained in clear, actionable language.From foundational principles to practical implementation, each chapter offers direct benefits: better models, stronger intuition, and the ability to turn raw data into useful predictions.Whether you're a student, a beginner stepping into machine learning, or a developer expanding your skill set, this book gives you the tools to create accurate, well-tested models using Python's most accessible and powerful library.If you're ready to build real machine-learning solutions with confidence, clarity, and accuracy, get your copy of BOOK TITLES today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

  • Lingua: Inglese

    Editore: Independently Published, 2025

    9798275717419

    • Brossura
    • Print on Demand

    Da: CitiRetail, Stevenage, Regno UnitoCitiRetail

    Venditore con 5 stelle
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    Condizione: Nuovo

    EUR 42,65

    EUR 43,17 spedizione 
    Spedito da Regno Unito a U.S.A.

    Quantità: 1 disponibili

    Paperback. Condizione: new. Paperback. What if you could understand machine-learning results with complete confidence-without getting lost in complicated math or confusing explanations?This book gives you that clarity.BOOK TITLES delivers a practical, beginner-friendly path to mastering real-world machine learning using Scikit-Learn, the most trusted toolkit in Python. At its core, this book solves a single problem: helping you move from "I understand the idea" to "I can actually build and evaluate models that work." Every chapter builds skill, accuracy, and confidence-without overwhelming theory.You'll quickly learn how to structure data, choose the right algorithm, train models efficiently, and evaluate them with meaningful metrics. Through clear explanations and hands-on examples, you'll understand why models behave the way they do and how to improve them with smarter preprocessing, tuning, and validation techniques.You'll be able to: - Build classification, regression, and clustering models that produce reliable results.- Apply essential preprocessing steps such as scaling, encoding, and feature selection.- Evaluate models using precision, recall, F1-score, confusion matrices, and cross-validation.- Strengthen your models through hyperparameter tuning, pipelines, and proper train/test practices.- Work effectively with real datasets and interpret outcomes with confidence.- Understand advanced topics such as ensemble methods, dimensionality reduction, and model optimization-explained in clear, actionable language.From foundational principles to practical implementation, each chapter offers direct benefits: better models, stronger intuition, and the ability to turn raw data into useful predictions.Whether you're a student, a beginner stepping into machine learning, or a developer expanding your skill set, this book gives you the tools to create accurate, well-tested models using Python's most accessible and powerful library.If you're ready to build real machine-learning solutions with confidence, clarity, and accuracy, get your copy of BOOK TITLES today. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.