Machine Learning with Python for Everyone, 1st edition

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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Pearson eTextbook: What’s on the inside just might surprise you

They say you can’t judge a book by its cover. It’s the same with your students. Meet each one right where they are with an engaging, interactive, personalized learning experience that goes beyond the textbook to fit any schedule, any budget, and any lifestyle.