Real-World Machine Learning (Paperback)

Before placing an order, please note:

  • You'll receive a confirmation email once your order is complete and ready for pickup. 
  • If you have a membership, please make a note of this in the order comments and we'll apply your discount.
  • Online orders are nonrefundable and cannot be exchanged.
  • If you place a pre-order to be shipped in the same order as currently available titles, an additional shipping fee will be added to your order.  
  • Women & Children First is not responsible for lost or stolen packages.
Real-World Machine Learning By Henrik Brink, Joseph Richards, Mark Fetherolf Cover Image



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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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.

Table of Contents

  1. What is machine learning?
  2. Real-world data
  3. Modeling and prediction
  4. Model evaluation and optimization
  5. Basic feature engineering

  1. Example: NYC taxi data
  2. Advanced feature engineering
  3. Advanced NLP example: movie review sentiment
  4. Scaling machine-learning workflows
  5. Example: digital display advertising



About the Author

Henrik Brink is a data scientist and software developer with extensive ML experience in industry and academia.

Joseph Richards is a senior data scientist with expertise in applied statistics and predictive analytics. Henrik and Joseph are co-founders of, a leading developer of machine learning solutions for industry.

Mark Fetherolf is founder and President of data management and predictive analytics company, Numinary Data Science. He has worked as a statistician and analytics database developer in social science research, chemical engineering, information systems performance, capacity planning, cable television, and online advertising applications.
Product Details
ISBN: 9781617291920
ISBN-10: 1617291927
Publisher: Manning
Publication Date: September 30th, 2016
Pages: 264
Language: English