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Real-World Machine Learning

Amazon.com Price:  $39.78 (as of 05/05/2019 14:20 PST- Details)

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Description

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 mean you can find valuable insights and patterns in data, which you would never recognize with traditional methods. In the real world, ML techniques come up with 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 wish to have 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 can build skills in data acquisition and modeling, classification, and regression. You can also explore crucial tasks like model validation, optimization, scalability, and real-time streaming. When you find yourself done, You can be able to successfully build, deploy, and handle 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 will have to 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

    6. 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

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