Real-World Machine Learning
Language: English
Published by Manning Publications, 2016
- Softcover
- Used
Seller: World of Books Inc, Montgomery, IL, U.S.A.World of Books Inc
AbeBooks seller since March 23, 2026
Condition: Used - Good
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Add to basketItem description from seller
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.…
Seller Inventory # CIN1617291927G
- Title
- Real-World Machine Learning
- Author
- Henrick Brink
- Publisher
- Manning Publications
- Publication year
- 2016
- Condition
- Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1617291927
- ISBN 13
- 9781617291920
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.
- 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
Code examples are in Python and R. No prior machine learning experience required.
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.
"Synopsis" may belong to another edition of this title.
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.
"About the title" may belong to another edition of this title.
World of Books Inc
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