Machine Learning with Python for Everyone, 1st edition

Published by Addison-Wesley Professional (July 30, 2019) © 2020

  • Mark Fenner

VitalSource eTextbook

ISBN-13: 9780134845647
Machine Learning with Python for Everyone
Published 2019
  • Available for purchase from all major ebook resellers, including InformIT.com

Paperback

ISBN-13: 9780134845623
Machine Learning with Python for Everyone
Published 2019

Details

  • A print text
  • Free shipping
  • Also available for purchase as an ebook from all major ebook resellers, including InformIT.com
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Access Details

  • Access courses online from any computer (PC or Mac) or tablet (Android or iOS)
  • Native app available for mobile use; use online, or download and work offline; data syncs automatically 
  • Purchase print or digital codes from your college bookstore, or printed access code cards here

Features

  • Interactive learning elements throughout, including exercises, quizzes, flashcards, and video tutorials

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.

  • Chapter 1: Let’s Discuss Learning
  • Chapter 2: Some Technical Background
  • Chapter 3: Predicting Categories: Getting Started with Classification
  • Chapter 4: Predicting Numerical Values: Getting Started with Regression
  • Part II: Evaluation
  • Chapter 5: Evaluating and Comparing Learners
  • Chapter 6: Evaluating Classifiers
  • Chapter 7: Evaluating Regressors
  • Part III: More Methods and Fundamentals
  • Chapter 8: More Classification Methods
  • Chapter 9: More Regression Methods
  • Chapter 10: Manual Feature Engineering: Manipulating Data for Fun and Profit
  • Chapter 11: Tuning Hyperparameters and Pipelines
  • Part IV: Adding Complexity
  • Chapter 12: Combining Learners
  • Chapter 13: Models That Engineer Features for Us
  • Chapter 14: Feature Engineering for Domains: Domain-Specific Learning
  • Chapter 15: Connections, Extensions, and Further Directions

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