Machine Learning with Python for Everyone, 1st edition

Published by Addison-Wesley Professional (November 19, 2021) © 2022

  • Mark Fenner

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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.

  • All students need to succeed in data science with Python: process, code, and implementation
  • Students will understand the machine learning process, leverage the powerful Python scikit-learn library, and master the algorithmic components of learning systems
  • Integrates clear narrative, carefully designed Python code, images, and interesting, intelligible datasets
  • Online resources for this book are available at the author's github site - https://github.com/mfenner1/mlwpy_code
  • 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
Dr. Mark Fenner, owner of Fenner Training and Consulting, LLC, has taught computing and mathematics to diverse adult audiences since 1999, and holds a PhD in computer science. His research has included design, implementation, and performance of machine learning and numerical algorithms; developing learning systems to detect user anomalies; and probabilistic modeling of protein function.

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