Machine Learning With Python For Everyone

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.

Machine Learning With Python For Everyone

Mark E. Fenner

AddisonWesley

2019

Abstract

Machine Learning With Python For Everyone
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.

Contents:
Part I: Frist steps

• 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

1. Chapter 5: Evaluating and Comparing Learners

2. Chapter 6: Evaluating Classifiers

3. 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

Citation

Mark E. Fenner, Machine Learning With Python For Everyone, AddisonWesley

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