Machine Learning
Hands-On for Developers and Technical Professionals
(Sprache: Englisch)
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...
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...
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Klappentext zu „Machine Learning “
Dig deep into the data with a hands-on guide to machine learningMachine 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
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data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
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Inhaltsverzeichnis zu „Machine Learning “
Introduction xixChapter 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
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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
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
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Autoren-Porträt von Jason Bell
Jason Bell has been working with point of sale and customer loyalty data since 2002 and has been involved in software development for more than 25 years. He works as a senior technical architect, lecturer and also advises startups that are just beginning their technical adventures.Bibliographische Angaben
- Autor: Jason Bell
- 2014, 1. Auflage, 408 Seiten, Maße: 18,9 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Wiley & Sons
- ISBN-10: 1118889061
- ISBN-13: 9781118889060
- Erscheinungsdatum: 30.12.2014
Sprache:
Englisch
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