Manning Early
Access Program
Real-World Machine Learning

EARLY ACCESS EDITION

Henrik Brink and Joseph W. Richards

MEAP Began: December 2013
Softbound print: Early 2015 (est.) | 400 pages
ISBN: 9781617291920

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Table of Contents, MEAP Chapters & Resources

Table of Contents         Resources 
  1 What is Machine Learning? - FREE
  2 Real World Data - AVAILABLE
  3 Modeling and Prediction - AVAILABLE
  4 Model Evaluation and Optimization - AVAILABLE
  5 Basic Feature Engineering - AVAILABLE
  6 Advanced Feature Engineering
  7 Unsupervised Learning
  8 Scaling With Size and Speed
  9 Streaming and Online Machine Learning
10 The Future of Machine Learning

Appendix
  A Popular Machine Learning Algorithms - AVAILABLE

DESCRIPTION

In a world where big data is the norm and near-real-time decisions are crucial, machine learning is a critical component of the data workflow. Machine learning systems can quickly crunch massive amounts of information to offer insight and make decisions in a way that matches or even surpasses human cognitive abilities. These systems use sophisticated computational and statistical tools to build models that can recognize and visualize patterns, predict outcomes, forecast values, and make recommendations. Gartner predicts that big data analytics will be a $25 billion market by 2017, and financial firms, marketing organizations, scientific facilities, and Silicon Valley startups are all demanding machine learning skills from their developers.

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. Using the Python language and the R statistical package, you'll start with core concepts like data acquisition and modeling, classification, and regression. You'll then move through the most important ML tasks, like model validation, optimization and feature engineering. By following numerous real-world examples, you'll learn how to anticipate and overcome common pitfalls. Along the way, you'll discover scalable and online algorithms for large and streaming data sets. Advanced readers will appreciate the in-depth discussion of enhanced ML systems through advanced data exploration and pre-processing methods.

WHAT'S INSIDE

Code examples are in Python and R. No prior machine learning experience required.

ABOUT THE AUTHORS

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 wise.io, a leading developer of machine learning solutions for industry.

ABOUT THE EARLY ACCESS VERSION

This Early Access version of Real-World Machine Learning enables you to receive new chapters as they are being written. You can also interact with the authors to ask questions, provide feedback and errata, and help shape the final manuscript on the Author Online forum.

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