
Title overview
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
Table of contents
- 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