Machine Learning
Hands-On for Developers and Technical Professionals
Published on 30. December 2014
Book
Paperback/Softback
408 pages
978-1-118-88906-0 (ISBN)
Description
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 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.
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Jason Bell
Machine Learning: Hands-On for Developers and Technical Professionals
Hands-On for Developers and Technical Professionals
Book
03/2020
2nd Edition
Wiley
€47.00
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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.
Content
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 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
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 Classifi cation Principles 140
Binary and Multiclass Classifi cation 140
Linear Classifi ers 142
Confidence 143
Maximizing and Minimizing to Find the Line 143
How Support Vector Machines Approach Classifi cation 144
Using Linear Classifi cation 144
Using Non-Linear Classifi cation 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
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
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
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
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 C 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: wc 361
Locating Anything: fi nd 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
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 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
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 Classifi cation Principles 140
Binary and Multiclass Classifi cation 140
Linear Classifi ers 142
Confidence 143
Maximizing and Minimizing to Find the Line 143
How Support Vector Machines Approach Classifi cation 144
Using Linear Classifi cation 144
Using Non-Linear Classifi cation 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
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
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
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
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 C 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: wc 361
Locating Anything: fi nd 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