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• Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface;
• Chapter 1: A Quick Start; Step by step; Signing up; Logging in; The welcome page; Things to know; Your account; Upgrading; Learning more; The shortcut panel bar; Explore, Predict, Assemble, and Refine; The content analytics architecture; The main components; Crawlers; Document processors; Indexers; Search engine; Miner (content analytics); Administration console; The flow of data; Exiting the flow; Deep inspection; Important concepts and terminologies; Structured versus unstructured

• Text analyticsSearching; Discovery; Mining; Collections; Facets; Frequency; Correlation; Deviation; Generally good advice; Hints; Join in; Summary;
• Chapter 2: Identifying Use Cases; Defining a use case; Importance of use cases; Working with Watson; What to ask of your data; Building questions; Putting data into context; Importance of data context; Use case examples; Summary;
• Chapter 3: Designing Solutions with Watson Analytics; Data considerations; The Content Analytics data model; A relational mindset; Structured and unstructured sources; Data categorized; Multiple data sources

• Date-sensitive dataExtracting information from textual data; Multiple collections; Building collections; The collection process
• step by step; Adding to collections from assemble; Planning for iteration; Programming interfaces; Programming with Watson Analytics; Summary; Chapter 4: Understanding Content Analysis; Basic concepts of Content Analytics; Manual or automation; Difficulties with textual analysis; Frequency and deviation; Precision and recall; Cycle of analysis with Watson Analytics; Defining a purpose; Obtaining the data; Performing the analysis; Determining actions to take

• ValidationA sample use case; Step 1: Define the purpose; Step 2: Obtaining the data; Step 3: Performing the analysis; Step 4: Determining actions to take; Step 5: Validation; Text data; Data metrics; Search and Filter; Summary;
• Chapter 5: Watson Analytics Predict and Assemble; Predict; Creating a Watson Analytics prediction; Viewing the results of a prediction; Predictor visualization bar; Main Insights; Details; Customization; Assemble; Views; Dashboards; Using templates; A simple use case; Some points of interest; Versioning; Assemble; Summary;
• Chapter 6: Customizing and Extending

• Meeting the requirementsReasons to customize or extend; Customizing Watson; Subscriptions; Data; Changing column types; Custom reaggregation; Customizing column names; Persistence; Views; Changing themes and presentation styles; Changing properties; Changing the media; Tabs, grouping, and new data; Extending Watson; Data quality; Watson data metrics; Using IBM SPSS; Handling missing values; An example use case; Summary;
• Chapter 7: Taking It to the Enterprise; Introducing an enterprise perspective; Definition of Watson knowledge; Data interpretation; Classification or grouping of data

Make the most advanced predictive analytical processes easy using Watson Analytics with this easy-to-follow practical guide About This Book * This is the first and the only book on IBM Watson Analytics, and it shows you how to leverage Watson in an enterprise environment through rich use cases * Incorporate Watson Analytics into your business strategy and confidently add this cutting edge expertise to your resume * This book is written by James D Miller, IBM-certified expert and accomplished Director and Sr. Project Leader Who This Book Is For If you want to perform data discovery and analysis and make sense of data you have, this book for you. Data scientists can also use this book to explore a new way to perform data analysis tasks on cloud with ease. This book does not require a programming background. What You Will Learn * Study the language of Watson while you discover how easy it is to access and configure * Review what a Watson use case is, why it's important, and how to identify one * Design Watson Analytical solutions based upon your use cases * Understand the basic concepts behind the content analysis cycle and where Watson fits in * Explore all the features of Watson, such as Explore, Predict, and Assemble * Customize and extend your Watson solutions * Use Watson at the Enterprise level * Integrate Watson with other toolsets In Detail Today, only a small portion of businesses actually use a real analytical tool as part of routine decision making. IBM Watson Analytics is changing that making the most advanced and predictive analytical techniques understandable and usable for any industry. This book will be the vital tour guide for your trip, starting with what IBM Watson Analytics is. We'll start off with introduction to Watson Analytics and then quickly move on to various use cases under which one can use the different analytics functionalities offered by Watson. During the course of the book, you will learn how to design solutions, and customize and extend Watson analytics. We will conclude by taking Watson Analytics to enterprise and integrating it with other solutions (other IBM solutions and analytics). Now is the time for you to learn IBM Watson to compete in the world. Style and approach Watson provides individuals with the ability to perform sophisticated data discovery and analysis without all of the complexity that usually goes along with it. This book will get you started with Watson analytics and how you can use it in day-to-day data analysis. The book introduces the key concepts and terminology and then uses practical use case examples to reinforce your understanding.
(source: Nielsen Book Data)

• Preface by John E. Kelly III
• 1. A New Era of Computing
• 2. Building Learning Systems
• 3. Handling Big Data
• 4. Augmenting Our Senses
• 5. Designing Data-centric Computers
• 6. Inventing a New Physics of Computing
• 7. Imagining the Cognitive City Coda: An Alliance of Human and Machine Notes.
• (source: Nielsen Book Data)

We are crossing a new frontier in the evolution of computing and entering the era of cognitive systems. The victory of IBM's Watson on the television quiz show Jeopardy! revealed how scientists and engineers at IBM and elsewhere are pushing the boundaries of science and technology to create machines that sense, learn, reason, and interact with people in new ways to provide insight and advice. In Smart Machines, John E. Kelly III, director of IBM Research, and Steve Hamm, a writer at IBM and a former business and technology journalist, introduce the fascinating world of "cognitive systems" to general audiences and provide a window into the future of computing. Cognitive systems promise to penetrate complexity and assist people and organizations in better decision making. They can help doctors evaluate and treat patients, augment the ways we see, anticipate major weather events, and contribute to smarter urban planning. Kelly and Hamm's comprehensive perspective describes this technology inside and out and explains how it will help us conquer the harnessing and understanding of "big data, " one of the major computing challenges facing businesses and governments in the coming decades. Absorbing and impassioned, their book will inspire governments, academics, and the global tech industry to work together to power this exciting wave in innovation.
(source: Nielsen Book Data)

Abstract The Building Cognitive Applications with IBM Watson Services series is a seven-volume collection that introduces IBM® Watson™ cognitive computing services. The series includes an overview of specific IBM Watson® services with their associated architectures and simple code examples. Each volume describes how you can use and implement these services in your applications through practical use cases. The series includes the following volumes: , SG24-8387 Volume 1 Getting Started , SG24-8394 Volume 2 Conversation , SG24-8393 Volume 3 Visual Recognition , SG24-8391 Volume 4 Natural Language Classifier , SG24-8392 Volume 5 Language Translator , SG24-8388 Volume 6 Speech to Text and Text to Speech , SG24-8398 Volume 7 Natural Language Understanding Whether you are a beginner or an experienced developer, this collection provides the information you need to start your research on Watson services. If your goal is to become more familiar with Watson in relation to your current environment, or if you are evaluating cognitive computing, this collection can serve as a powerful learning tool. This IBM Redbooks® publication, Volume 2, describes how the Watson Conversation service can be used to create chatbots and user agents that understand natural-language input and communicate with your users simulating a real human conversation. It introduces the concepts that you need to understand in order to use the Watson Conversation service. It provides examples of applications that integrate the Watson Conversation service with other IBM Bluemix® services, such as the IBM IoT Platform, Text to Speech, Speech to Text, and Weather Company® Data, to implement practical use cases. You can develop and deploy the sample applications by following along in a step-by-step approach and using provided code snippets. Alternatively, you can download an existing Git project to more quickly deploy the application.

• Table of Contents Introduction to IBM Cloud Feature Extraction - A Bag of Tricks Supervised Machine Learning Models for Your Data Implementing Unsupervised Algorithms Machine Learning Workouts on IBM Cloud Using SPARK with IBM Watson Studio Deep Learning Using TensorFlow on the IBM Cloud Creating a Facial Expression Platform on the IBM Cloud Automated Classification of Lithofacies Formation Using Machine Learning Building a Cloud-Based Multi-Biometric Identity Authentication Platform.
• (source: Nielsen Book Data)

Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services Key Features Implement data science and machine learning techniques to draw insights from real-world data Understand what IBM Cloud platform can help you to implement cognitive insights within applications Understand the role of data representation and feature extraction in any machine learning system Book DescriptionIBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This book is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python. Hands-On Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You'll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The book will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later chapters, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies. By the end of this book, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples. What you will learn Understand key characteristics of IBM machine learning services Run supervised and unsupervised techniques in the cloud Understand how to create a Spark pipeline in Watson Studio Implement deep learning and neural networks on the IBM Cloud with TensorFlow Create a complete, cloud-based facial expression classification solution Use biometric traits to build a cloud-based human identification system Who this book is forThis beginner-level book is for data scientists and machine learning engineers who want to get started with IBM Cloud and its machine learning services using practical examples. Basic knowledge of Python and some understanding of machine learning will be useful.
(source: Nielsen Book Data)

Whether you are a beginner or an experienced developer, this IBM Rebooks publication provides the information you need to start your research on Watson services. -- Edited summary from book.

This book covers key concepts in content analytics, such as facets, frequency, deviation, correlation, trend, and sentimental analysis. It describes the content analytics miner, and guides you on performing content analytics using views, dictionary lookup, and customization. The book also covers using IBM Content Analytics Studio for domain-specific content analytics, integrating with IBM Content Classification to get categories and new metadata, interfacing with IBM Cognos Business Intelligence (BI) to add values in BI reporting and analysis, and customizing the content analytics miner with APIs. In addition, the book describes how to use the enterprise search capability for the discovery and retrieval of documents using various query and visual navigation techniques, and customization of crawling, parsing, indexing, and runtime search to improve search results. The target audience of this book is decision makers, business users, and IT architects and specialists who want to understand and analyze their enterprise content to improve and enhance their business operations. It is also intended as a technical how-to guide for use with the online IBM Knowledge Center for configuring and performing content analytics and enterprise search with Content Analytics."-- Unedited summary from book

• Preface by John E. Kelly III
• 1. A New Era of Computing
• 2. Building Learning Systems
• 3. Handling Big Data
• 4. Augmenting Our Senses
• 5. Designing Data-centric Computers
• 6. Inventing a New Physics of Computing
• 7. Imagining the Cognitive City Coda: An Alliance of Human and Machine Notes.
• (source: Nielsen Book Data)

We are crossing a new frontier in the evolution of computing and entering the era of cognitive systems. The victory of IBM's Watson on the television quiz show Jeopardy! revealed how scientists and engineers at IBM and elsewhere are pushing the boundaries of science and technology to create machines that sense, learn, reason, and interact with people in new ways to provide insight and advice. In Smart Machines, John E. Kelly III, director of IBM Research, and Steve Hamm, a writer at IBM and a former business and technology journalist, introduce the fascinating world of "cognitive systems" to general audiences and provide a window into the future of computing. Cognitive systems promise to penetrate complexity and assist people and organizations in better decision making. They can help doctors evaluate and treat patients, augment the ways we see, anticipate major weather events, and contribute to smarter urban planning. Kelly and Hamm's comprehensive perspective describes this technology inside and out and explains how it will help us conquer the harnessing and understanding of "big data, " one of the major computing challenges facing businesses and governments in the coming decades. Absorbing and impassioned, their book will inspire governments, academics, and the global tech industry to work together to power this exciting wave in innovation.
(source: Nielsen Book Data)

• Table of Contents Introduction to IBM Cloud Feature Extraction - A Bag of Tricks Supervised Machine Learning Models for Your Data Implementing Unsupervised Algorithms Machine Learning Workouts on IBM Cloud Using SPARK with IBM Watson Studio Deep Learning Using TensorFlow on the IBM Cloud Creating a Facial Expression Platform on the IBM Cloud Automated Classification of Lithofacies Formation Using Machine Learning Building a Cloud-Based Multi-Biometric Identity Authentication Platform.
• (source: Nielsen Book Data)

Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services Key Features Implement data science and machine learning techniques to draw insights from real-world data Understand what IBM Cloud platform can help you to implement cognitive insights within applications Understand the role of data representation and feature extraction in any machine learning system Book DescriptionIBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This book is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python. Hands-On Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You'll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The book will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later chapters, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies. By the end of this book, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples. What you will learn Understand key characteristics of IBM machine learning services Run supervised and unsupervised techniques in the cloud Understand how to create a Spark pipeline in Watson Studio Implement deep learning and neural networks on the IBM Cloud with TensorFlow Create a complete, cloud-based facial expression classification solution Use biometric traits to build a cloud-based human identification system Who this book is forThis beginner-level book is for data scientists and machine learning engineers who want to get started with IBM Cloud and its machine learning services using practical examples. Basic knowledge of Python and some understanding of machine learning will be useful.
(source: Nielsen Book Data)

Whether you are a beginner or an experienced developer, this IBM Rebooks publication provides the information you need to start your research on Watson services. -- Edited summary from book.

Whether you are a beginner or an experienced developer, this IBM Rebooks publication provides the information you need to start your research on Watson services. -- Edited summary from book.

• Table of Contents The Essentials of IBM Watson A Basic Watson Project An Automated Supply Chain Scenario Healthcare Dialoguing Social Media Sentiment Analysis Pattern Recognition and Classification Retail and Personalized Recommendations Integration for Sales Forecasting Anomaly Detection in Banking Using AI What's Next.
• (source: Nielsen Book Data)

Incorporate intelligence to your data-driven business insights and high accuracy business solutions Key Features Explore IBM Watson capabilities such as Natural Language Processing (NLP) and machine learning Build projects to adopt IBM Watson across retail, banking, and healthcare Learn forecasting, anomaly detection, and pattern recognition with ML techniques Book DescriptionIBM Watson provides fast, intelligent insight in ways that the human brain simply can't match. Through eight varied projects, this book will help you explore the computing and analytical capabilities of IBM Watson. The book begins by refreshing your knowledge of IBM Watson's basic data preparation capabilities, such as adding and exploring data to prepare it for being applied to models. The projects covered in this book can be developed for different industries, including banking, healthcare, media, and security. These projects will enable you to develop an AI mindset and guide you in developing smart data-driven projects, including automating supply chains, analyzing sentiment in social media datasets, and developing personalized recommendations. By the end of this book, you'll have learned how to develop solutions for process automation, and you'll be able to make better data-driven decisions to deliver an excellent customer experience. What you will learn Build a smart dialog system with cognitive assistance solutions Design a text categorization model and perform sentiment analysis on social media datasets Develop a pattern recognition application and identify data irregularities smartly Analyze trip logs from a driving services company to determine profit Provide insights into an organization's supply chain data and processes Create personalized recommendations for retail chains and outlets Test forecasting effectiveness for better sales prediction strategies Who this book is forThis book is for data scientists, AI engineers, NLP engineers, machine learning engineers, and data analysts who wish to build next-generation analytics applications. Basic familiarity with cognitive computing and sound knowledge of any programming language is all you need to understand the projects covered in this book.
(source: Nielsen Book Data)

Whether you are a beginner or an experienced developer, this IBM Rebooks publication provides the information you need to start your research on Watson services. -- Edited summary from book.

• Table of Contents Background, Transition and the Future of Computing Can Machines Converse like Humans? Computer Vision This Is How Computers Speak Expecting Empathy from Dumb Computers Language - How Watson deals with NL Structuring Unstructured Content Through Watson Putting It All Together with Watson Future: Cognitive Computing and You.
• (source: Nielsen Book Data)

Understand, design, and create cognitive applications using Watson's suite of APIs. Key Features Develop your skills and work with IBM Watson APIs to build efficient and powerful cognitive apps Learn how to build smart apps to carry out different sets of activities using real-world use cases Get well versed with the best practices of IBM Watson and implement them in your daily work Book DescriptionCognitive computing is rapidly infusing every aspect of our lives riding on three important fields: data science, machine learning (ML), and artificial intelligence (AI). It allows computing systems to learn and keep on improving as the amount of data in the system grows. This book introduces readers to a whole new paradigm of computing - a paradigm that is totally different from the conventional computing of the Information Age. You will learn the concepts of ML, deep learning (DL), neural networks, and AI through the set of APIs provided by IBM Watson. This book will help you build your own applications to understand, plan, and solve problems, and analyze them as per your needs. You will learn about various domains of cognitive computing, such as NLP, voice processing, computer vision, emotion analytics, and conversational systems, using different IBM Watson APIs. From this, the reader will learn what ML is, and what goes on in the background to make computers "do their magic, " as well as where these concepts have been applied. Having achieved this, the readers will then be able to embark on their journey of learning, researching, and applying the concept in their respective fields. What you will learn Get well versed with the APIs provided by IBM Watson on IBM Cloud Learn ML, AI, cognitive computing, and neural network principles Implement smart applications in fields such as healthcare, entertainment, security, and more Understand unstructured content using cognitive metadata with the help of Natural Language Understanding Use Watson's APIs to create real-life applications to realize their capabilities Delve into various domains of cognitive computing, such as media analytics, embedded deep learning, computer vision, and more Who this book is forThis book is for beginners and novices; having some knowledge about artificial intelligence and deep learning is an advantage, but not a prerequisite to benefit from this book. We explain the concept of deep learning and artificial intelligence through the set of tools IBM Watson provides.
(source: Nielsen Book Data)

The Building Cognitive Applications with IBM Watson Services series is a seven-volume collection that introduces IBM® Watson"!cognitive computing services. The series includes an overview of specific IBM Watson® services with their associated architectures and simple code examples. Each volume describes how you can use and implement these services in your applications through practical use cases. The series includes the following volumes: Volume 1 Getting Started, SG24-8387 Volume 2 Conversation, SG24-8394 Volume 3 Visual Recognition, SG24-8393 Volume 4 Natural Language Classifier, SG24-8391 Volume 5 Language Translator, SG24-8392 Volume 6 Speech to Text and Text to Speech, SG24-8388 Volume 7 Natural Language Understanding, SG24-8398 Whether you are a beginner or an experienced developer, this collection provides the information you need to start your research on Watson services. If your goal is to become more familiar with Watson in relation to your current environment, or if you are evaluating cognitive computing, this collection can serve as a powerful learning tool. This IBM Redbooks® publication, Volume 4, introduces the Watson Natural Language Classifier service. This service applies cognitive computing techniques to return best matching predefined classes for short text inputs such as a sentence or phrase. The book describes concepts that you need to understand to create, use and train the classifier. This book describes how to prepare training data, and create and train the classifier to connect the classes to example texts so the service can apply the classes to new inputs. It provides examples of applications that demonstrate how to use the Watson Natural Language Classifier service in practical use cases. You can develop and deploy the sample applications by following along in a step-by-step approach and using provided code snippets. Alternatively, you can download an existing Git project to more quickly deploy the application

Whether you are a beginner or an experienced developer, this IBM Rebooks publication provides the information you need to start your research on Watson services. -- Edited summary from book.

"Data scientist and entrepreneur, Matt Coatney, provides a hype free overview of IBM Watson in this course covering Watson's technology and key machine learning capabilities. You'll learn how to access Watson's key services, how to converse with Watson, and how to extract structure and meaning from content using Watson's natural language processing (NLP) functions. Coatney offers real life use cases and code samples to provide business executives and software developers with the tools they need to bring cognitive computing into their applications. As a result of this course, strategic decision makers and technologists will know how to integrate IBM Watson into their own particular industries and businesses."--Resource description page.

• Introduction The Query Process and Barriers to Finding Information Online Online Search: An Evolution Search and Discovery Technologies: An Overview Information Access: A Spectrum of Needs and Uses Future Tense: The Next Era in Information Access and Discovery Answer Machines.
• (source: Nielsen Book Data)

The Answer Machine is a practical, non-technical guide to the technologies behind information seeking and analysis. It introduces search and content analytics to software buyers, knowledge managers, and searchers who want to understand and design effective online environments. The book describes how search evolved from an expert-only to an end user tool. It provides an overview of search engines, categorization and clustering, natural language processing, content analytics, and visualization technologies. Detailed profiles for Web search, eCommerce search, eDiscovery, and enterprise search contrast the types of users, uses, tasks, technologies, and interaction designs for each. These variables shape each application, although the underlying technologies are the same. Types of information tasks and the trade-offs between precision and recall, time, volume and precision, and privacy vs. personalization are discussed within this context. The book examines trends toward convenient, context-aware computing, big data and analytics technologies, conversational systems, and answer machines. The Answer Machine explores IBM Watson's DeepQA technology and describes how it is used to answer health care and Jeopardy questions. The book concludes by discussing the implications of these advances: how they will change the way we run our businesses, practice medicine, govern, or conduct our lives in the digital age.
(source: Nielsen Book Data)

• Introduction The Query Process and Barriers to Finding Information Online Online Search: An Evolution Search and Discovery Technologies: An Overview Information Access: A Spectrum of Needs and Uses Future Tense: The Next Era in Information Access and Discovery Answer Machines.
• (source: Nielsen Book Data)

The Answer Machine is a practical, non-technical guide to the technologies behind information seeking and analysis. It introduces search and content analytics to software buyers, knowledge managers, and searchers who want to understand and design effective online environments. The book describes how search evolved from an expert-only to an end user tool. It provides an overview of search engines, categorization and clustering, natural language processing, content analytics, and visualization technologies. Detailed profiles for Web search, eCommerce search, eDiscovery, and enterprise search contrast the types of users, uses, tasks, technologies, and interaction designs for each. These variables shape each application, although the underlying technologies are the same. Types of information tasks and the trade-offs between precision and recall, time, volume and precision, and privacy vs. personalization are discussed within this context. The book examines trends toward convenient, context-aware computing, big data and analytics technologies, conversational systems, and answer machines. The Answer Machine explores IBM Watson's DeepQA technology and describes how it is used to answer health care and Jeopardy questions. The book concludes by discussing the implications of these advances: how they will change the way we run our businesses, practice medicine, govern, or conduct our lives in the digital age.
(source: Nielsen Book Data)