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
PART 1: THE MACHINE-LEARNING WORKFLOW
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What is machine learning?
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Real-world data
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Modeling and prediction
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Model evaluation and optimization
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Basic feature engineering
PART 2: PRACTICAL APPLICATION
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Example: NYC taxi data
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Advanced feature engineering
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Advanced NLP example: movie review sentiment
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Scaling machine-learning workflows
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Example: digital display advertising
