Produktbild: Applied Machine Learning

Applied Machine Learning Using Machine Learning to Solve Business Problems

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Beschreibung

Produktdetails

Verkaufsrang

31397

Einband

Taschenbuch

Erscheinungsdatum

05.05.2026

Verlag

Rheinwerk Publishing

Seitenzahl

440

Maße (L/B/H)

25,4/17,9/2,4 cm

Gewicht

782 g

Farbe

Seidengrau / Lila

Auflage

1

Sprache

Englisch

ISBN

978-1-4932-2758-7

Beschreibung

Produktdetails

Verkaufsrang

31397

Einband

Taschenbuch

Erscheinungsdatum

05.05.2026

Verlag

Rheinwerk Publishing

Seitenzahl

440

Maße (L/B/H)

25,4/17,9/2,4 cm

Gewicht

782 g

Farbe

Seidengrau / Lila

Auflage

1

Sprache

Englisch

ISBN

978-1-4932-2758-7

Herstelleradresse

Rheinwerk Verlag GmbH
Rheinwerkallee 4
53227 Bonn
DE

Email: service@rheinwerk-verlag.de

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  • Produktbild: Applied Machine Learning
  • ... Preface ... 13

    ... Who Is This Book For? ... 13

    ... The Structure of This Book ... 16

    ... Conclusion ... 18

    1 ... Introduction ... 19

    1.1 ... Aligning on Nomenclature ... 19

    1.2 ... Learning to Google (or Prompt) ... 21

    1.3 ... Predictions for Generative AI’s Impact on Machine Learning ... 26

    1.4 ... Summary ... 26

    2 ... Getting Started ... 27

    2.1 ... GitHub ... 27

    2.2 ... Anaconda ... 30

    2.3 ... Summary ... 38

    3 ... Introduction to Our Use Cases ... 39

    3.1 ... Importance of Understanding the Business Problem ... 39

    3.2 ... Use Case 1: The Retail Tyrant ... 41

    3.3 ... Use Case 2: Customer Retention ... 47

    3.4 ... Use Case 3: Crime Predictions ... 50

    3.5 ... Summary ... 53

    4 ... Starting with the Data ... 55

    4.1 ... Types of Data Sources ... 55

    4.2 ... Data Exploration ... 66

    4.3 ... Data Cleaning (For Now) ... 120

    4.4 ... Summary ... 178

    5 ... Picking Your Model ... 181

    5.1 ... The Simpler the Model, the Better ... 181

    5.2 ... Model Decision Framework ... 183

    5.3 ... Train-Test Split ... 187

    5.4 ... Regression Models ... 189

    5.5 ... Machine Learning Models ... 221

    5.6 ... Clustering ... 291

    5.7 ... Summary ... 297

    6 ... Evaluating the Model and Iterating ... 299

    6.1 ... Importance of Picking Validation Metrics ... 299

    6.2 ... Validation Metrics ... 301

    6.3 ... K-Fold Cross-Validation ... 311

    6.4 ... Business Validations ... 311

    6.5 ... Machine Learning Interpretability ... 314

    6.6 ... Iterating on the Model ... 321

    6.7 ... Application to Use Cases ... 328

    6.8 ... Summary ... 374

    7 ... Implementing, Monitoring, and Measuring the Model ... 375

    7.1 ... Implementing Your Model for Predictions ... 375

    7.2 ... Model Monitoring ... 394

    7.3 ... Measuring the Impact of Your Model ... 401

    7.4 ... Summary ... 426

    8 ... Closing Thoughts ... 427

    8.1 ... Learning How to Learn with Generative AI ... 427

    8.2 ... Learning How to Learn with Use Cases ... 428

    8.3 ... Explore and Visualize Your Data ... 428

    8.4 ... Cleaning Your Data and Dummy Coding ... 429

    8.5 ... Machine Learning Models ... 430

    8.6 ... Hyperparameters and Grid Search ... 430

    8.7 ... Variable Lagging ... 431

    8.8 ... The End ... 431

    8.9 ... Acknowledgments ... 431

    ... The Author ... 433

    ... Index ... 435