Bookstore

Dig deep into the data with a hands-on guide to machine learning
Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference.
At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to:
Learn the languages of machine learning including Hadoop, Mahout, and Weka
Understand decision trees, Bayesian networks, and artificial neural networks
Implement Association Rule, Real Time, and Batch learning
Develop a strategic plan for safe, effective, and efficient machine learning
By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.

Machine Learning: Hands-On for Developers and Technical Professionals

Introduction xix

Chapter 1 What Is Machine Learning? 1

History of Machine Learning 1

Alan Turing 1

Arthur Samuel 2

Tom M. Mitchell 2

Summary Definition 2

Algorithm Types for Machine Learning 3

Supervised Learning 3

Unsupervised Learning 3

The Human Touch 4

Uses for Machine Learning 4

Software 4

Stock Trading 5

Robotics 6

Medicine and Healthcare 6

Advertising 6

Retail and E-Commerce 7

Gaming Analytics 8

The Internet of Things 9

Languages for Machine Learning 10

Python 10

R 10

Matlab 10

Scala 10

Clojure 11

Ruby 11

Software Used in This Book 11

Checking the Java Version 11

Weka Toolkit 12

Mahout 12

SpringXD 13

Hadoop 13

Using an IDE 14

Data Repositories 14

UC Irvine Machine Learning Repository 14

Infochimps 14

Kaggle 15

Summary 15

Chapter 2 Planning for Machine Learning 17

The Machine Learning Cycle 17

It All Starts with a Question 18

I Don't Have Data! 19

Starting Local 19

Competitions 19

One Solution Fits All? 20

Defining the Process 20

Planning 20

Developing 21

Testing 21

Reporting 21

Refining 22

Production 22

Building a Data Team 22

Mathematics and Statistics 22

Programming 23

Graphic Design 23

Domain Knowledge 23

Data Processing 23

Using Your Computer 24

A Cluster of Machines 24

Cloud-Based Services 24

Data Storage 25

Physical Discs 25

Cloud-Based Storage 25

Data Privacy 25

Cultural Norms 25

Generational Expectations 26

The Anonymity of User Data 26

Don't Cross "The Creepy Line" 27

Data Quality and Cleaning 28

Presence Checks 28

Type Checks 29

Length Checks 29

Range Checks 30

Format Checks 30

The Britney Dilemma 30

What's in a Country Name? 33

Dates and Times 35

Final Thoughts on Data Cleaning 35

Thinking about Input Data 36

Raw Text 36

Comma Separated Variables 36

JSON 37

YAML 39

XML 39

Spreadsheets 40

Databases 41

Thinking about Output Data 42

Don't Be Afraid to Experiment 42

Summary 43

Chapter 3 Working with Decision Trees 45

The Basics of Decision Trees 45

Uses for Decision Trees 45

Advantages of Decision Trees 46

Limitations of Decision Trees 46

Different Algorithm Types 47

How Decision Trees Work 48

Decision Trees in Weka 53

The Requirement 53

Training Data 53

Using Weka to Create a Decision Tree 55

Creating Java Code from the Classifi cation 60

Testing the Classifi er Code 64

Thinking about Future Iterations 66

Summary 67

Chapter 4 Bayesian Networks 69

Pilots to Paperclips 69

A Little Graph Theory 70

A Little Probability Theory 72

Coin Flips 72

Conditional Probability 72

Winning the Lottery 73

Bayes' Theorem 73

How Bayesian Networks Work 75

Assigning Probabilities 76

Calculating Results 77

Node Counts 78

Using Domain Experts 78

A Bayesian Network Walkthrough 79

Java APIs for Bayesian Networks 79

Planning the Network 79

Coding Up the Network 81

Summary 90

Chapter 5 Artificial Neural Networks 91

What Is a Neural Network? 91

Artificial Neural Network Uses 92

High-Frequency Trading 92

Credit Applications 93

Data Center Management 93

Robotics 93

Medical Monitoring 93

Breaking Down the Artifi cial Neural Network 94

Perceptrons 94

Activation Functions 95

Multilayer Perceptrons 96

Back Propagation 98

Data Preparation for Artifi cial Neural Networks 99

Artificial Neural Networks with Weka 100

Generating a Dataset 100

Loading the Data into Weka 102

Configuring the Multilayer Perceptron 103

Training the Network 105

Altering the Network 108

Increasing the Test Data Size 108

Implementing a Neural Network in Java 109

Create the Project 109

The Code 111

Converting from CSV to Arff 114

Running the Neural Network 114

Summary 115

Chapter 6 Association Rules Learning 117

Where Is Associati