Real-world machine learning / Henrik Brink, Joseph W. Richards, Mark Fetherolf.

Format:

• Book

Author/Creator:

• Brink, Henrik, author.
• Richards, Joseph W., author.
• Fetherolf, Mark, author.

Language:

English

Subjects (All):

• Machine learning.

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Place of Publication:

Shelter Island, New York : Manning Publications, [2017]

System Details:

text file

Summary:

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 powerful ML systems. About the Technology Machine learning systems help you find valuable insights and patterns in data, which you’d never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It’s a hot and growing field, and up-to-speed ML developers are in demand. About the Book Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you’ll build skills in data acquisition and modeling, classification, and regression. You’ll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you’re done, you’ll be ready to successfully build, deploy, and maintain your own powerful ML systems. What’s Inside Predicting future behavior Performance evaluation and optimization Analyzing sentiment and making recommendations About the Reader No prior machine learning experience assumed. Readers should know Python. About the Authors Henrik Brink, Joseph Richards, and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning.

Contents:

• 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.

Notes:

• Includes index.
• Description based on print version record.

ISBN:

• 9781638357001
• 1638357005
• 9781617291920
• 1617291927

OCLC:

961944484