Real-world machine learning

Real-world machine learning

Summary Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy...

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Detalles Bibliográficos
Otros Autores: Brink, Henrik, author (author), Richards, Joseph W., author, Fetherolf, Mark, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Shelter Island, New York : Manning Publications [2017]
Edición:1st edition
Materias:
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629882806719
Tabla de Contenidos:
  • Intro
  • Copyright
  • Brief Table of Contents
  • Table of Contents
  • Foreword
  • Preface
  • Acknowledgments
  • About this Book
  • About the Authors
  • About the Cover Illustration
  • Part 1. The machine-learning workflow
  • Chapter 1. What is machine learning?
  • Chapter 2. Real-world data
  • Chapter 3. Modeling and prediction
  • Chapter 4. Model evaluation and optimization
  • Chapter 5. Basic feature engineering
  • Part 2. Practical application
  • Chapter 6. Example: NYC taxi data
  • Chapter 7. Advanced feature engineering
  • Chapter 8. Advanced NLP example: movie review sentiment
  • Chapter 9. Scaling machine-learning workflows
  • Chapter 10. Example: digital display advertising
  • Appendix. Popular machine-learning algorithms
  • Index
  • List of Figures
  • List of Tables
  • List of Listings.

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