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Machine Learning in Python : Essential Techniques for Predictive Analysis.

Author: Michael Bowles
Publisher: Hoboken : Wiley, 2015.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Bowles, Michael.
Machine Learning in Python : Essential Techniques for Predictive Analysis.
Hoboken : Wiley, ©2015
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Michael Bowles
ISBN: 9781118961759 1118961757
OCLC Number: 908080090
Notes: Variable Importance Information.
Description: 1 online resource (361 pages)
Contents: Machine Learning in Python℗ʼ; Contents; Introduction; Chapter 1 The Two Essential Algorithms for Making Predictions; Why Are These Two Algorithms So Useful?; What Are Penalized Regression Methods?; What Are Ensemble Methods?; How to Decide Which Algorithm to Use; The Process Steps for Building a Predictive Model; Framing a Machine Learning Problem; Feature Extraction and Feature Engineering; Determining Performance of a Trained Model; Chapter Contents and Dependencies; Summary; Chapter 2 Understand the Problem by Understanding the Data; The Anatomy of a New Problem. Different Types of Attributes and Labels Drive Modeling ChoicesThings to Notice about Your New Data Set; Classification Problems: Detecting Unexploded Mines Using Sonar; Physical Characteristics of the Rocks Versus Mines Data Set; Statistical Summaries of the Rocks versus Mines Data Set; Visualization of Outliers Using Quantile-Quantile Plot; Statistical Characterization of Categorical Attributes; How to Use Python Pandas to Summarize the Rocks Versus Mines Data Set; Visualizing Properties of the Rocks versus Mines Data Set; Visualizing with Parallel Coordinates Plots. Visualizing Interrelationships between Attributes and LabelsVisualizing Attribute and Label Correlations Using a Heat Map; Summarizing the Process for Understanding Rocks versus Mines Data Set; Real-Valued Predictions with Factor Variables: How Old Is Your Abalone?; Parallel Coordinates for Regression Problems-Visualize Variable Relationships for Abalone Problem; How to Use Correlation Heat Map for Regression-Visualize Pair-Wise Correlations for the Abalone Problem; Real-Valued Predictions Using Real-Valued Attributes: Calculate How Your Wine Tastes. Multiclass Classification Problem: What Type of Glass Is That?Summary; Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data; The Basic Problem: Understanding Function Approximation; Working with Training Data; Assessing Performance of Predictive Models; Factors Driving Algorithm Choices and Performance-Complexity and Data; Contrast Between a Simple Problem and a Complex Problem; Contrast Between a Simple Model and a Complex Model; Factors Driving Predictive Algorithm Performance; Choosing an Algorithm: Linear or Nonlinear? Measuring the Performance of Predictive ModelsPerformance Measures for Different Types of Problems; Simulating Performance of Deployed Models; Achieving Harmony Between Model and Data; Choosing a Model to Balance Problem Complexity, Model Complexity, and Data Set Size; Using Forward Stepwise Regression to Control Overfitting; Evaluating and Understanding Your Predictive Model; Control Overfitting by Penalizing Regression Coefficients-Ridge Regression; Summary; Chapter 4 Penalized Linear Regression; Why Penalized Linear Regression Methods Are So Useful; Extremely Fast Coefficient Estimation.

Abstract:

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, d.

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