Machine learning for decision makers: Artificial Intelligence in the age of the Internet of Things, Big Data, and the Cloud

✍ Scribed by Apress L.P.; Kashyap, Dr. Patanjali

• Publisher: Apress
• Year: 2018;2017
• Tongue: English
• Leaves: 381
• Category: Library

✦ Synopsis

Take a deep dive into the high-level concepts of machine learning. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry.Machine Learning for Decision Makersserves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other.
This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making.
The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business.

What You Will Learn
Discover the machine learning, big data, and cloud and cognitive computing technology stack


Gain insights into machine learning concepts and practices
Understand business and enterprise decision-making using machine learning
Absorb machine-learning best practices

Who This Book Is For
CIOs, COOs, CTOs, CFOs, and CEOs who are the key decision-makers in an organization. This book will also help IT professionals who want to learn about machine learning, big data technologies, and IoT.

✦ Table of Contents

Contents......Page 5
About the Author......Page 14
About the Technical Reviewer......Page 15
Foreword......Page 16
Preface......Page 18
Acknowledgments......Page 22
Introduction......Page 24
Chapter 1: Let’s Integrate with Machine Learning......Page 33
Your Business, My Technology, and Our Interplay of Thoughts......Page 34
General Introduction to Machine Learning......Page 35
The Details of Machine Learning......Page 37
Supervised Learning......Page 40
Unsupervised Learning......Page 41
Current Business Challenges for Machine Learning......Page 42
The Needs and Business Drivers of Machine Learning......Page 43
What Are Big Data and Big Data Analytics?......Page 44
Velocity......Page 46
What Is Analytics......Page 47
Essential Characteristics of Cloud Computing......Page 49
Deployment Models......Page 50
Service Models......Page 51
Challenges of Cloud Computing......Page 53
What Is IoT?......Page 54
Evolution, Development, and the Future of IoT......Page 55
Characteristics of the Internet of Things......Page 56
Challenges of the Internet of Things......Page 57
How IoT Works......Page 58
What Is Cognitive Computing?......Page 59
How Cognitive Computing Works......Page 61
Characteristics of Cognitive Computing......Page 62
How the Cloud, IoT, Machine Learning, Big Data Analytics, and Cognitive Computing Work Together......Page 63
Mind Map......Page 66
Chapter 2: The Practical Concepts of Machine Learning......Page 67
Linking History, Evolution, Machine Learning, and Artificial Intelligence......Page 68
Machine Learning, AI, the Brain, and the Business of Intelligence......Page 71
General Architecture of Machine Learning......Page 73
Technology Related to Machine Learning......Page 75
Need for Machine Learning......Page 77
Machine Learning Business Opportunities......Page 78
Automated Machine Learning Based Customer Support Systems......Page 82
Machine Learning Customer Retention Systems......Page 84
Intelligent Customer Prioritization and Classification Products, Applications, and Services (APS)......Page 87
Autonomous Systems......Page 88
Emotions and Sentiment Analysis Based APS......Page 90
Recommendations Based Applications, Products, and Services (APS)......Page 91
Financial and Insurance Services......Page 92
Telecom Network, Products, and Services......Page 93
Public Sector and Government Initiatives......Page 94
Transport......Page 95
Manufacturing......Page 96
Machine Learning for Legal Activities......Page 97
Science and Technology......Page 98
Space Science......Page 99
Biology......Page 100
Reinforcement Learning......Page 101
Semi-Supervised Learning: A Quick Look......Page 103
Binary Classification Model......Page 104
Tools for Machine Learning......Page 105
Frameworks for Machine Learning......Page 108
Large-Scale Machine Learning......Page 109
Programming Languages for Machine Learning......Page 110
R......Page 111
Scala......Page 112
Python......Page 114
Latest Advancements in Machine Learning......Page 116
Case Studies......Page 119
Mind Map......Page 121
Reference, Web Links, Notes and Bibliography......Page 122
Algorithms, Algorithms, Everywhere......Page 123
Classification of Machine Learning Algorithm......Page 125
Clustering......Page 126
Regression......Page 127
Classification......Page 128
Anomaly Detection......Page 130
How to Select the Right Algorithm/Model for Your Requirements......Page 132
Choosing the Correct Alogorithm......Page 133
Step 3: Cross-Validation......Page 134
Step 6: Determining Appropriate Objectives and Business Value......Page 135
Step 7: Learn and Develop Flexibility, Adaptability, Innovation, and Out of the Box Thinking......Page 136
A Review of Some Important Machine Learning Algorithms......Page 137
Random Forest Algorithm......Page 138
Success Stories......Page 139
Advantages of Decision Trees......Page 140
Applications of Decision Trees......Page 141
Logistic (Classification) and Linear Regression......Page 142
Advantages of Logistic Regression......Page 143
Applications of Logistic Regression......Page 144
Support Vector Machine Algorithms......Page 145
Applications of SVM......Page 146
Naïve Bayes......Page 147
Applications of Naïve Bayes......Page 148
k-means Clustering......Page 149
Advantages of k-means......Page 150
Success Stories......Page 151
Applications of Apriori......Page 152
Markov and Hidden Markov Models......Page 153
Bayesian Network and Artificial Neural Network (ANN)......Page 154
Applications of ANN......Page 156
Machine Learning Application Building......Page 157
Why Do You Need Agile?......Page 158
Show Me Some Water Please …......Page 159
Some Machine Learning Algorithms Based Products and Applications......Page 160
New Shared Economy-Based Business Models......Page 162
Macro-Level Changes and Disrupted Economy......Page 163
The Marriage of IoT, Big Data Analytics, Machine Learning, and Industrial Security......Page 164
Industry 4.0: IoT and Machine Learning Algorithms......Page 165
Before Winding Up......Page 167
Mind Map......Page 168
Chapter 4: Technology Stack for Machine Learning and Associated Technologies......Page 169
Software Stacks......Page 170
Internet of Things Technology Stack......Page 174
Device and Sensor Layer......Page 175
Communication, Protocol, and Transportation Layers......Page 178
Data Processing Layer......Page 180
Presentation and Application Layer......Page 181
IoT Solution Availability......Page 182
Big Data Analytics Technology Stack......Page 183
Hadoop Distributed File System (HDFS)......Page 186
The Core Hadoop Architecture......Page 187
Amazon Simple Storage Service (S3)......Page 188
Analytics Layer......Page 189
Hadoop MapReduce......Page 190
Apache Hive......Page 192
HBase......Page 194
MangoDB......Page 197
Apache Solr......Page 198
HDInsight......Page 199
Presentation and Application Layer......Page 200
Offerings from Vendors in the Big Data Space......Page 202
Machine Learning Technology Stack......Page 204
Connector Layer......Page 205
Apache Kafka......Page 206
Processing Layer......Page 207
Apache Mahout......Page 208
Microsoft Cognitive Toolkit......Page 209
Presentation and Application Layer......Page 210
Role of Cloud Computing in the Machine Learning Technology Stack......Page 212
Cognitive Computing Technology Stack......Page 213
The Cloud Computing Technology Stack......Page 217
Audio and Video Links......Page 218
Mind Map......Page 219
Chapter 5: Industrial Applications of Machine Learning......Page 220
Data, Machine Learning, and Analytics......Page 221
What Is Machine Learning Analytics?......Page 223
Challenges Associated with Machine Learning Analytics......Page 224
Business Drivers of Machine Learning Analytics......Page 225
Challenges in Implementing Machine Learning in the Manufacturing Industry......Page 226
Drivers of Machine Learning Analytics for the Manufacturing Industry......Page 227
Machine Learning Based Analytics: Applications in the Manufacturing Industry......Page 228
Other Uses of Machine Learning Analytics in the Manufacturing Industry......Page 229
Challenges of Implementing Machine Learning Analytics in Bank and Financial Institutions......Page 230
Drivers of Machine Learning Analytics for Financial Institutions......Page 231
Machine Learning Based Analytics: Applications in Financial Institutions......Page 232
Other Uses of Machine Learning Analytics in Financial Institutions......Page 234
Machine Learning Based Healthcare Analytics......Page 235
Challenges in Implementing Machine Learning Analytics to the Healthcare Sector......Page 237
Drivers of Machine Learning Analytics in the Healthcare Industry......Page 238
Machine Learning Based Analytics: Applications in the Healthcare Industry......Page 239
Other Uses of Machine Learning Analytics in the Healthcare Industry......Page 242
Machine Learning Based Marketing Analytics......Page 243
Drivers of Machine Learning Analytics for Marketing......Page 244
Machine Learning Based Analytics: Applications in Marketing Analytics......Page 245
Other Uses of Machine Learning Analytics in Marketing......Page 247
Challenges in Implementing Machine Learning Analytics in the Retail Industry......Page 248
Drivers of Machine Learning Analytics in the Retail Industry......Page 249
Machine Learning Analytics Based Analytics: Applications in the Retail Industry......Page 250
Customer Machine Learning Analytics......Page 251
Challenges in Implementing Customer Machine Learning Analytics......Page 252
Other Uses of Customer Machine Learning Analytics......Page 253
Disaster and Hazards Management......Page 255
Aviation......Page 256
Advertising......Page 257
Insurance......Page 258
Summary......Page 263
Mind Map......Page 264
Chapter 6: I Am the Future: Machine Learning in Action......Page 265
State of the Art......Page 266
Siri......Page 267
IBM Watson......Page 268
Microsoft Cortana......Page 269
Connected Cars......Page 271
Highlights of the Connected Car System......Page 272
Driverless Cars......Page 273
Virtual, Immersive, Augmented Reality......Page 275
Google Now......Page 277
SAP Leonardo......Page 278
Salesforce Einstein......Page 280
Security and Machine Learning......Page 281
Quantum Machine Learning......Page 284
Practical Innovations......Page 285
Machine Learning Adoption Scorecard......Page 286
Summary......Page 289
Mind Map......Page 290
IT, Machine Learning, Vendors, Clients, and Changing Times......Page 291
Designing Key Performance Indicators (KPIs) for Machine Learning Analytics Based Domains......Page 294
Designing Effective KPIs Using a Balanced Scorecard......Page 296
Measurement Categories......Page 297
Some Important KPIs from Specific Organization and Industry Perspectives......Page 299
Industry-Specific KPIs......Page 300
Differences Between KPIs and Metrics......Page 301
Risk, Compliances, and Machine Learning......Page 302
Risk and Risk Management Processes for Machine Learning Projects......Page 303
Risk Identification......Page 304
Monitoring and Controlling Risks......Page 305
Best Practices for Machine Learning......Page 306
Evolving Technologies and Machine Learning......Page 307
Summary......Page 308
Mind Map......Page 309
Chapter 8: Do Not Forget Me: The Human Side of Machine Learning......Page 310
Economy, Workplace, Knowledge, You, and Technology......Page 311
Bottom-Up Innovation......Page 313
Spirituality......Page 314
Measuring Intelligence......Page 315
Benefits of These Competencies......Page 322
EQ, SQ, MQ, and Social Q and Building an Efficient ML Team......Page 324
Team Leader......Page 326
Team Members......Page 327
Organizational Leader......Page 328
How to Build Data Culture for Machine Learning......Page 329
Role 1: Deep Learning/Machine Learning Engineer......Page 332
Role 2: Data Scientist......Page 334
Other Important Roles......Page 335
Lean Project Management and Machine Learning Projects......Page 337
How to Do the Right Resourcing and Find the Best Match......Page 339
The Need for DevOps......Page 341
Summary......Page 342
Mind Map......Page 343
Chapter 9: Let’s Wrap Up: The Final Destination......Page 344
Appendix A:How to Architect and Build a Machine Learning Solution......Page 348
Architectural Considerations......Page 350
Blueprinting and Machine Learning Projects......Page 351
Appendix B:A Holistic Machine Learning and Agile-Based Software Methodology......Page 353
Proposed Software Process and Model......Page 354
Solution......Page 355
The Process......Page 356
Relevance and Future Direction of the Model......Page 357
Appendix C:Data Processing Technologies......Page 358
Bibliography......Page 360
Index......Page 373