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Machine Learning with Python for Everyone

Machine Learning with Python for Everyone

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  • Estimated Release: Jul 19, 2019
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  • Copyright 2020
  • Dimensions: 7" x 9-1/8"
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-484565-X
  • ISBN-13: 978-0-13-484565-4

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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Sample Content

Table of Contents

List of Figures
List of Tables

Part I: First 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
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 Hyper-Parameters 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
Appendix: mlwpy.py Listing



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