Machine Learning with Python for Everyone

The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python

Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning.

Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use.

• Understand machine learning algorithms, models, and core machine learning concepts
• Classify examples with classifiers, and quantify examples with regressors
• Realistically assess performance of machine learning systems
• Use feature engineering to smooth rough data into useful forms
• Chain multiple components into one system and tune its performance
• Apply machine learning techniques to images and text
• Connect the core concepts to neural networks and graphical models
• Leverage the Python scikit-learn library and other powerful tools

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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.

Preface

 

Part I: End-to-End Machine Learning with Python: Take One
1. An Introduction to Machine Learning Systems and Data
2. Getting Started with Classification: Predicting Classes or Groups
3. Getting Started with Regression: Predicting Values

 

Part II: How Did We Do: Evaluation of Learning Systems
1. Evaluating and Comparing Learners
2. Evaluating Classifiers: Did We Build It Right?
3. Evaluating Regressors: Did We Build It Right?

 

Part III: The Usual Suspects: Standard Learning Methods and Their Use in Python
1. More Classification Methods
2. More Regression Methods
3. Connections between Learners

 

Part IV: Beyond the Basics: Additional Learners and Data Manipulation
1. Combining Learners: Ensemble Methods
2. When the Raw Features Aren’t Enough: Feature Engineering
3. Domain Specific Feature Engineering

 

Part IV: End-to-End Machine Learning with Python: The Final Cut
1. A Brief Survey of Real World Issues
2. Graduation Day for Classifiers: Realistic Classification
3. Graduation Day for Regressors: Realistic Regression
4. What Next?

 

Appendix