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 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

THE MACHINE-LEARNING WORKFLOW
1. What is machine learning?
2. Real-world data
3. Modeling and prediction
4. Model evaluation and optimization
5. Basic feature engineering
PRACTICAL APPLICATION
6. Example: NYC taxi data
7. Advanced feature engineering
8. Advanced NLP example: movie review sentiment
9. Scaling machine-learning workflows
10. 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 wise.io, 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.

Summary

DESCRIPTION

In a world where big data is the norm and near-real-time decisions are crucial, machine learning (ML) is a critical component of the data workflow. Machine learning systems can quickly crunch massive amounts of information to offer insights 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.
 
Real-World Machine Learning is a practical guide designed to teach developers the art of ML project execution. The book introduces the day-to-day practice of machine learning and prepares readers to successfully build and deploy powerful ML systems. Using the Python language and the R statistical package, it starts with core concepts like data acquisition and modeling, classification, and regression. Then it moves through the most important ML tasks, like model validation, optimization and feature engineering. It uses real-world examples that help readers anticipate and overcome common pitfalls. Along the way, they will 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.

 

KEY FEATURES

• Accessible and  practical introduction to machine learning
• Contains big-picture ideas and real-world examples
• Prepares reader to build and deploy powerful predictive systems
• Offers tips & tricks and highlights common pitfalls

AUDIENCE

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

ABOUT THE TECHNOLOGY

Machine learning has gained prominence due to the overwhelming successes of Google, Microsoft, Amazon, LinkedIn, Facebook, and others in their use of ML. The Gartner report 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.

Product details

• Authors: Brink, Henrick Brink, Henrik Brink, Mark Fetherolf, Henrik Brink, Joseph Richards, Mark Fetherolf, Joesph Richards, Joesph W. Richards, Joseph Richards, Joseph W. Richards
• Publisher: Pearson
• Languages: English
• Product format: Paperback / Softback
• Released: 31.12.2017
• EAN: 9781617291920
• ISBN: 978-1-61729-192-0
• No. of pages: 264
• Dimensions: 187 mm x 252 mm x 15 mm
• Weight: 450 g
• Series: Manning; Manning
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT