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Machine Learning in Python: Essential Techniques for Predictive Analysis

Book Description

Learn a simpler and more effective way to analyze data and predict outcomes with Python

Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions.

Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language.

  • Predict outcomes using linear and ensemble algorithm families

  • Build predictive models that solve a range of simple and complex problems

  • Apply core machine learning algorithms using Python

  • Use sample code directly to build custom solutions

  • Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics.

    Table of Contents

    1. Introduction
      1. Who This Book Is For
      2. What This Book Covers
      3. How This Book Is Structured
      4. What You Need to Use This Book
      5. Conventions
      6. Source Code
      7. Errata
    2. Chapter 1 The Two Essential Algorithms for Making Predictions
      1. Why Are These Two Algorithms So Useful?
      2. What Are Penalized Regression Methods?
      3. What Are Ensemble Methods?
      4. How to Decide Which Algorithm to Use
      5. The Process Steps for Building a Predictive Model
      6. Chapter Contents and Dependencies
      7. Summary
      8. References
    3. Chapter 2 Understand the Problem by Understanding the Data
      1. The Anatomy of a New Problem
      2. Classification Problems: Detecting Unexploded Mines Using Sonar
      3. Visualizing Properties of the Rocks versus Mines Data Set
      4. Real-Valued Predictions with Factor Variables: How Old Is Your Abalone?
      5. Real-Valued Predictions Using Real-Valued Attributes: Calculate How Your Wine Tastes
      6. Multiclass Classification Problem: What Type of Glass Is That?
      7. Summary
      8. Reference
    4. Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data
      1. The Basic Problem: Understanding Function Approximation
      2. Factors Driving Algorithm Choices and Performance—Complexity and Data
      3. Measuring the Performance of Predictive Models
      4. Achieving Harmony Between Model and Data
      5. Summary
      6. References
    5. Chapter 4 Penalized Linear Regression
      1. Why Penalized Linear Regression Methods Are So Useful
      2. Penalized Linear Regression: Regulating Linear Regression for Optimum Performance
      3. Solving the Penalized Linear Regression Problem
      4. Extensions to Linear Regression with Numeric Input
      5. Summary
      6. References
    6. Chapter 5 Building Predictive Models Using Penalized Linear Methods
      1. Python Packages for Penalized Linear Regression
      2. Multivariable Regression: Predicting Wine Taste
      3. Binary Classification: Using Penalized Linear Regression to Detect Unexploded Mines
      4. Multiclass Classification: Classifying Crime Scene Glass Samples
      5. Summary
      6. References
    7. Chapter 6 Ensemble Methods
      1. Binary Decision Trees
      2. Bootstrap Aggregation: “Bagging”
      3. Gradient Boosting
      4. Random Forest
      5. Summary
      6. References
    8. Chapter 7 Building Ensemble Models with Python
      1. Solving Regression Problems with Python Ensemble Packages
      2. Coding Bagging to Predict Wine Taste
      3. Incorporating Non-Numeric Attributes in Python Ensemble Models
      4. Solving Binary Classification Problems with Python Ensemble Methods
      5. Solving Multiclass Classification Problems with Python Ensemble Methods
      6. Comparing Algorithms
      7. Summary
      8. References
    9. Title page
    10. Copyright
    11. Dedication
    12. About the Author
    13. About the Technical Editor
    14. Credits
    15. Acknowledgments
    16. EULA