|Real-World Machine Learning
Henrik Brink, Joseph W. Richards, and Mark Fetherolf
MEAP Began: December 2013
Softbound print: December 2015 (est.) | 400 pages | B&W
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Table of Contents, MEAP Chapters & Resources
|Table of Contents||Resources|
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 Example: NYC Taxi Data - AVAILABLE
7 Advanced Feature Engineering - AVAILABLE
8 Scaling With Size and Speed
9 Scaling Machine Learning Workflows - AVAILABLE
10 The Future of Machine Learning
A Popular Machine Learning Algorithms - AVAILABLE
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.
- Learn to build and maintain your own ML system
- Explore real-world machine-learning problems
- Detailed treatment of many example real-world use-cases
- Understand the ML workflow, practical considerations and common pitfalls
- Python and R code snippets to get you started
- Advanced material: feature engineering, computational scalability, and real-time streaming ML
- Beautiful visuals throughout
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. 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 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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