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