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, the author teaches through carefully-crafted datasets that are complex enough to be interesting, but simple enough for non-specialists. Building on this foundation, the book 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:
Chapter 1: Let’s Discuss Learning
Chapter 2: Predicting Categories: Getting Started with Classification
Chapter 3: Predicting Numerical Values: Getting Started with Regression
Chapter 4: Evaluating and Comparing Learners
Chapter 5: Evaluating Classifiers
Chapter 6: Evaluating Regressors
Chapter 7: More Classification Methods
Chapter 8: More Regression Methods
Chapter 9: Manual Feature Engineering: Manipulating Data for Fun and Profit
Chapter 10: Models That Engineer Features for Us
Chapter 11: Feature Engineering for Domains: Domain-Specific Learning
Online Chapters
Chapter 12: Tuning Hyperparameters and Pipelines
Chapter 13: Combining Learners
Chapter 14: Connections, Extensions, and Further Directions
About the Author
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.
