Machine learning: hands-on for developers and technical professionals
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Indianapolis, IN
Wiley
2015
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | XXIV, 380 S. Ill. |
| ISBN: | 9781118889060 |
Internformat
MARC
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| 035 | |a (OCoLC)900663955 | ||
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| 100 | 1 | |a Bell, Jason |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Machine learning |b hands-on for developers and technical professionals |c Jason Bell |
| 264 | 1 | |a Indianapolis, IN |b Wiley |c 2015 | |
| 300 | |a XXIV, 380 S. |b Ill. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
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| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-118-88939-8 |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-118-88949-7 |
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Datensatz im Suchindex
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|---|---|
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| adam_text | Contents
Introduction
xix
Chapter
1
What Is Machine Learning?
1
History of Machine Learning
1
Alan Turing
ι
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
iq
Clojure
И
Ruby
w
IX
Contents
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
Contents xi
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 Classification
60
Testing the Classifier 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
xii Contents
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 Artificial Neural Network
94
Perceptrons
94
Activation Functions
95
Multilayer Perceptrons
96
Back Propagation
98
Data Preparation for Artificial 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 Association Rules Learning Used?
117
Web Usage Mining
118
Beer and Diapers
118
How Association Rules Learning Works
119
Support
121
Confidence
121
Lift
122
Conviction
122
Defining the Process
122
Contents
xiii
Algorithms
123
Apriori
123
FP-Growth
124
Mining
the Baskets
—
A Walkthrough
124
Downloading the Raw Data
124
Setting Up the Project in Eclipse
125
Setting Up the Items Data File
126
Setting Up the Data
129
Running Mahout
131
Inspecting the Results
133
Putting It All Together
135
Further Development
136
Summary
137
Chapter
7
Support Vector Machines
139
What Is a Support Vector Machine?
139
Where Are Support Vector Machines Used?
140
The Basic Classification Principles
140
Binary and Multiclass Classification
140
Linear Classifiers
142
Confidence
143
Maximizing and Minimizing to Find the Line
143
How Support Vector Machines Approach Classification
144
Using Linear Classification
144
Using Non-Linear Classification
146
Using Support Vector Machines in
Weka
147
Installing LibSVM
147
A Classification Walkthrough
148
Implementing LibSVM with Java
154
Summary
159
Chapter
8
Clustering
161
What Is Clustering?
161
Where Is Clustering Used?
162
The Internet
162
Business and Retail
163
Law Enforcement
163
Computing
163
Clustering Models
164
How the K-Means Works
164
Calculating the Number of Clusters in
a
Dataset
166
K-Means Clustering with
Weka
168
Preparing the Data
168
The Workbench Method
169
The Command-Line Method
174
The Coded Method
178
Summary
186
xiv Contents
Chapter
9
Machine Learning in Real Time with Spring XD
187
Capturing the Firehose of Data
187
Considerations of Using Data in Real Time
188
Potential Uses for a Real-Time System
188
Using Spring XD
189
Spring XD Streams
190
Input Sources, Sinks, and Processors
190
Learning from Twitter Data
193
The Development Plan
193
Configuring the Twitter API Developer Application
194
Configuring Spring XD
196
Starting the Spring XD Server
197
Creating Sample Data
198
The Spring XD Shell
198
Streams
101 199
Spring XD and Twitter
202
Setting the Twitter Credentials
202
Creating Your First Twitter Stream
203
Where to Go from Here
205
Introducing Processors
206
How Processors Work within a Stream
206
Creating Your Own Processor
207
Real-Time Sentiment Analysis
215
How the Basic Analysis Works
215
Creating a Sentiment Processor
217
Spring XD Taps
221
Summary
222
Chapter
10
Machine Learning as a Batch Process
223
Is It Big Data?
223
Considerations for Batch Processing Data
224
Volume and Frequency
224
How Much Data?
225
Which Process Method?
225
Practical Examples of Batch Processes
225
Hadoop
225
Sqoop
226
Pig
226
Mahout
226
Cloud-Based Elastic Map Reduce
226
A Note about the Walkthroughs
227
Using the Hadoop Framework
227
The Hadoop Architecture
227
Setting Up a Single-Node Cluster
229
Contents xv
How MapReduce
Works 233
Mining
the Hashtags
234
Hadoop Support in Spring XD 235
Objectives for This Walkthrough
235
What s a Hashtag?
235
Creating the MapReduce Classes
236
Performing ETL on Existing Data
247
Product Recommendation with Mahout
250
Mining Sales Data
256
Welcome to My Coffee Shop!
257
Going Small Scale
258
Writing the Core Methods
258
Using Hadoop and MapReduce
260
Using Pig to Mine Sales Data
263
Scheduling Batch Jobs
273
Summary
274
Chapter
11
Apache Spark
275
Spark: A Hadoop Replacement?
275
Java,
Scala, or
Python?
276
Scala
Crash Course
276
Installing
Scala
276
Packages
277
Data Types
277
Classes
278
Calling Functions
278
Operators
279
Control Structures
279
Downloading and Installing Spark
280
A Quick Intro to Spark
280
Starting the Shell
281
Data Sources
282
Testing Spark
282
Spark Monitor
284
Comparing Hadoop MapReduce to Spark
285
Writing Standalone Programs with Spark
288
Spark Programs in
Scala
288
Installing SBT
288
Spark Programs in Java
291
Spark Program Summary
295
Spark SQL
295
Basic Concepts
295
Using SparkSQL with RDDs
296
Spark Streaming
305
Basic Concepts
305
Creating Your First Stream with
Scala
306
Creating Your First Stream with Java
309
xvi Contents
MLib:
The Machine Learning Library
311
Dependencies
311
Decision Trees
312
Clustering
313
Summary
313
Chapter
12
Machine Learning with
R
315
Installing
R
315
Mac OSX
315
Windows
316
Linux
316
Your First Run
316
Installing R-Studio
317
The
R
Basics
318
Variables and Vectors
318
Matrices
319
Lists
320
Data Frames
321
Installing Packages
322
Loading in Data
323
Plotting Data
324
Simple Statistics
327
Simple Linear Regression
329
Creating the Data
329
The Initial Graph
329
Regression with the Linear Model
330
Making a Prediction
331
Basic Sentiment Analysis
331
Functions to Load in Word Lists
331
Writing a Function to Score Sentiment
332
Testing the Function
333
Apriori
Association Rules
333
Installing the ARules Package
334
The Training Data
334
Importing the Transaction Data
335
Running the
Apriori
Algorithm
336
Inspecting the Results
336
Accessing
R
from Java
337
Installing the rjava Package
337
Your First Java Code in
R
337
Calling
R
from Java Programs
338
Setting Up an Eclipse Project
338
Creating the Java/R Class
339
Running the Example
340
Extending Your
R
Implementations
342
R
and Hadoop
342
Contents
xvii
The RHadoop Project
342
A Sample Map Reduce Job in RHadoop
343
Connecting to Social Media with
R
345
Summary
347
Appendix A SpringXD Quick Start
349
Installing Manually
349
Starting SpringXD
349
Creating a Stream
350
Adding a Twitter Application Key
350
Appendix
B Hadoop
1.x Quick Start
351
Downloading and Installing Hadoop
351
Formatting the HDFS
Filesystem
352
Starting and Stopping Hadoop
353
Process List of a Basic Job
353
Appendix
С
Useful Unix Commands
355
Using Sample Data
355
Showing the Contents: cat, more, and less
356
Example Command
356
Expected Output
356
Filtering Content:
grep
357
Example Command for Finding Text
357
Example Output
357
Sorting Data: sort
358
Example Command for Basic Sorting
358
Example Output
358
Finding Unique Occurrences: uniq
360
Showing the Top of a File: head
361
Counting Words: we
361
Locating Anything: find
362
Combining Commands and Redirecting Output
363
Picking a Text Editor
363
Colon Frenzy:
Vi
and Vim
363
Nano
364
Emacs
364
Appendix
D
Further Reading
367
Machine Learning
367
Statistics
368
Big Data and Data Science
368
Hadoop
368
Visualization
369
Making Decisions
369
Datasets
369
Blogs
370
Useful Websites
370
The Tools of the Trade
370
Index
373
Go deep data diving with this hands-on
guide to machine learning
If you want to get into machine learning but fear the math, this book is your ultimate guide. Specifically
designed for non-mathematicians, this useful guide presents a breakdown of each variant of machine
learning, with examples and working code. You ll learn the various algorithms, data preparation tech¬
niques, trees, and networks, and get acquainted with the tools that help you get more from your data.
You ll understand how it works, where it s used, and how to make it great.
•
Learn the languages of machine learning:
Weka,
Mahout™, Spark™, and
R
•
Make the right data storage and cleaning decisions, tailored to your desired output
•
Understand decision trees, Bayesian networks, artificial neural networks, and
association rule learning
•
•
Apply Big Data processing techniques with Hadoop-, Mahout, and MapReduce
•
Use Spring XD to capture streaming data and learn in real time
•
Access the tools you need to plan your project and acquire and process data
•
or more
than
25
years. He works as a senior technical architect, lecturer and also advises startups that are just beginning their technical adventures.
|
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| id | DE-604.BV042144398 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T04:01:08Z |
| institution | BVB |
| isbn | 9781118889060 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027584308 |
| oclc_num | 900663955 |
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| physical | XXIV, 380 S. Ill. |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Wiley |
| record_format | marc |
| spellingShingle | Bell, Jason Machine learning hands-on for developers and technical professionals Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Machine learning hands-on for developers and technical professionals |
| title_auth | Machine learning hands-on for developers and technical professionals |
| title_exact_search | Machine learning hands-on for developers and technical professionals |
| title_full | Machine learning hands-on for developers and technical professionals Jason Bell |
| title_fullStr | Machine learning hands-on for developers and technical professionals Jason Bell |
| title_full_unstemmed | Machine learning hands-on for developers and technical professionals Jason Bell |
| title_short | Machine learning |
| title_sort | machine learning hands on for developers and technical professionals |
| title_sub | hands-on for developers and technical professionals |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Maschinelles Lernen |
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