Machine Learning with Spark and Python 2nd Edition by Michael Bowles ISBN 1119561930 978-1119561934
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ISBN 10: 1119561930
ISBN 13: 978-1119561934
Author: Michael Bowles
Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark―a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code.
Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.
Machine Learning with Spark and Python 2nd Table of contents:
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
Chapter Contents and Dependencies
Summary
References
CHAPTER 2: Understand the Problem by Understanding the Data
The Anatomy of a New Problem
Classification Problems: Detecting Unexploded Mines Using Sonar
Visualizing Properties of the Rocks Versus Mines Data Set
Real-Valued Predictions with Factor Variables: How Old Is Your Abalone?
Real-Valued Predictions Using Real-Valued Attributes: Calculate How Your Wine Tastes
Multiclass Classification Problem: What Type of Glass Is That?
Using PySpark to Understand Large Data Sets
Summary
Reference
CHAPTER 3: Predictive Model Building: Balancing Performance, Complexity, and Big Data
The Basic Problem: Understanding Function Approximation
Factors Driving Algorithm Choices and Performance—Complexity and Data
Measuring the Performance of Predictive Models
Achieving Harmony between Model and Data
Using PySpark for Training Penalized Regression Models on Extremely Large Data Sets
Summary
Reference
CHAPTER 4: Penalized Linear Regression
Why Penalized Linear Regression Methods Are So Useful
Penalized Linear Regression: Regulating Linear Regression for Optimum Performance
Solving the Penalized Linear Regression Problem
Extension of Linear Regression to Classification Problems
Summary
References
CHAPTER 5: Building Predictive Models Using Penalized Linear Methods
Python Packages for Penalized Linear Regression
Multivariable Regression: Predicting Wine Taste
Binary Classification: Using Penalized Linear Regression to Detect Unexploded Mines
Multiclass Classification: Classifying Crime Scene Glass Samples
Linear Regression and Classification Using PySpark
Using PySpark to Predict Wine Taste
Logistic Regression with PySpark: Rocks Versus Mines
Incorporating Categorical Variables in a PySpark Model: Predicting Abalone Rings
Multiclass Logistic Regression with Meta Parameter Optimization
Summary
References
CHAPTER 6: Ensemble Methods
Binary Decision Trees
Bootstrap Aggregation: “Bagging”
Gradient Boosting
Random Forests
Summary
References
CHAPTER 7: Building Ensemble Models with Python
Solving Regression Problems with Python Ensemble Packages
Incorporating Non-Numeric Attributes in Python Ensemble Models
Solving Binary Classification Problems with Python Ensemble Methods
Solving Multiclass Classification Problems with Python Ensemble Methods
Solving Regression Problems with PySpark Ensemble Packages
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