9780134845623

Machine Learning with Python for Everyone

by
  • ISBN13:

    9780134845623

  • ISBN10:

    0134845625

  • Edition: 1st
  • Format: Paperback
  • Copyright: 2018-06-30
  • Publisher: Addison-Wesley Professional
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Supplemental Materials

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  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

Summary

Business analysts, managers, researchers, and students are rushing to master powerful machine learning techniques for improving decision-making and scaling analysis to immense datasets. Machine Learning with Python for Everyone brings together all they’ll need to succeed: a practical understanding of the machine learning process, accessible code, skills for implementing that process with Python and the scikit-learn library, and real expertise in using learning systems intelligently.

 

Reflecting 20 years of experience teaching non-specialists, Dr. Mark Fenner teaches through carefully-crafted datasets that are complex enough to be interesting, but simple enough for non-specialists. Building on this foundation, Fenner presents real-world case studies that apply his lessons in detailed, nuanced ways. Throughout, he offers clear narratives, practical “code-alongs,” and easy-to-understand images -- focusing on mathematics only where it’s necessary to make connections and deepen insight.

Table of Contents

Preface

 

Part I: End-to-End Machine Learning with Python: Take One
1. An Introduction to Machine Learning Systems and Data
2. Getting Started with Classification: Predicting Classes or Groups
3. Getting Started with Regression: Predicting Values

 

Part II: How Did We Do: Evaluation of Learning Systems
1. Evaluating and Comparing Learners
2. Evaluating Classifiers: Did We Build It Right?
3. Evaluating Regressors: Did We Build It Right?

 

Part III: The Usual Suspects: Standard Learning Methods and Their Use in Python
1. More Classification Methods
2. More Regression Methods
3. Connections between Learners

 

Part IV: Beyond the Basics: Additional Learners and Data Manipulation
1. Combining Learners: Ensemble Methods
2. When the Raw Features Aren’t Enough: Feature Engineering
3. Domain Specific Feature Engineering

 

Part IV: End-to-End Machine Learning with Python: The Final Cut
1. A Brief Survey of Real World Issues
2. Graduation Day for Classifiers: Realistic Classification
3. Graduation Day for Regressors: Realistic Regression
4. What Next?

 

Appendix

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