• Produktbild: The Art of Machine Learning
  • Produktbild: The Art of Machine Learning
  • Produktbild: The Art of Machine Learning
  • Produktbild: The Art of Machine Learning
  • Produktbild: The Art of Machine Learning
  • Produktbild: The Art of Machine Learning
  • Produktbild: The Art of Machine Learning

The Art of Machine Learning A Hands-On Guide to Machine Learning with R

59,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

09.01.2024

Verlag

No Starch Press,US

Seitenzahl

272

Maße (L/B/H)

23,5/18/2 cm

Gewicht

534 g

Farbe

Vanille / Schwarz

Sprache

Englisch

ISBN

978-1-71850-210-9

Beschreibung

Rezension

"In contrast to other books about machine learning, there is a bigger emphasis on programming and usage in practice. In particular, there is an excellent explanation of how to avoid over/under-fitting, and how to use cross-validation. This book is sure to be helpful for students who are interested to understand the core concepts, as well as their practical implementations in R."
Toby Dylan Hocking, Assistant Professor, Northern Arizona University

"The Art of Machine Learning by Norman Matloff is a welcome addition to a growing body of books about machine learning. Matloff, whose career spans both computer science and statistics, addresses the new and exciting field with a fresh approach."
Dirk Eddelbuettel, Department of Statistics, University of Illinois

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

09.01.2024

Verlag

No Starch Press,US

Seitenzahl

272

Maße (L/B/H)

23,5/18/2 cm

Gewicht

534 g

Farbe

Vanille / Schwarz

Sprache

Englisch

ISBN

978-1-71850-210-9

EU-Ansprechpartner

Kolibri 360 GmbH
Ettore-Bugatti-Straße 6-14
51149 Köln
DE
produktsicherheit@kolibri360.de

Herstelleradresse

No Starch Press
400 Hahn Road
21157 Westminster
UK
info@nostarch.com

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  • Produktbild: The Art of Machine Learning
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  • Acknowledgments
    Introduction

    PART I: PROLOGUE, AND NEIGHBORHOOD-BASED METHODS
    Chapter 1: Regression Models
    Chapter 2: Classification Models
    Chapter 3: Bias, Variance, Overfitting, and Cross-Validation
    Chapter 4: Dealing with Large Numbers of Features
    PART II: TREE-BASED METHODS
    Chapter 5: A Step Beyond k-NN: Decision Trees
    Chapter 6: Tweaking the Trees
    Chapter 7: Finding a Good Set of Hyperparameters
    PART III: METHODS BASED ON LINEAR RELATIONSHIPS
    Chapter 8: Parametric Methods
    Chapter 9: Cutting Things Down to Size: Regularization
    PART IV: METHODS BASED ON SEPARATING LINES AND PLANES
    Chapter 10: A Boundary Approach: Support Vector Machines
    Chapter 11: Linear Models on Steroids: Neural Networks
    PART V: APPLICATIONS
    Chapter 12: Image Classification 
    Chapter 13: Handling Time Series and Text Data 
    Appendix A: List of Acronyms and Symbols 
    Appendix B: Statistics and ML Terminology Correspondence
    Appendix C: Matrices, Data Frames, and Factor Conversions
    Appendix D: Pitfall: Beware of “p-Hacking”!