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IBM Watson Projects

Book Description

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 Description

IBM 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 for

This 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.

Table of Contents

  1. Title Page
  2. Copyright and Credits
    1. IBM Watson Projects
  3. Packt Upsell
    1. Why subscribe?
    2. Packt.com
  4. Contributors
    1. About the author
    2. About the reviewer
    3. Packt is searching for authors like you
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
      1. Download the example code files
      2. Conventions used
    4. Get in touch
      1. Reviews
  6. The Essentials of IBM Watson
    1. Definition and objectives
    2. IBM Cloud prerequisites
    3. Exploring the Watson interface
      1. The menu bar
        1. Menu icon
        2. IBM Cloud
        3. Catalog
        4. Docs
        5. Support
        6. Manage
        7. Profile – avatar
      2. Online glossary, let's chat, and feedback
      3. What about Watson?
      4. The Watson dashboard
      5. Menu bar
      6. Quick start information bar
      7. Search, add, filter, and sort
      8. Content panel area
    4. Basic tasks refresher
      1. The first step
      2. Explore
        1. Watson prompts
      3. Predict
      4. Assemble
      5. Social media
      6. Refine
      7. Saving the original
      8. Add – some data
      9. Refine
    5. Summary
  7. A Basic Watson Project
    1. The problem defined
    2. Getting started
      1. Gathering data
    3. Building your Watson project
      1. Loading your data
      2. Data review
      3. What does this mean?
      4. Improving your score with Refine
    4. Refine or Explore
    5. Creating a prediction
      1. Top predictors
      2. Main Insight page
      3. Details page
      4. An insight
      5. Reviewing the results
    6. Summary
  8. An Automated Supply Chain Scenario
    1. The problem defined
    2. Getting started
      1. Gathering and reviewing  data
    3. Building the Watson project
      1. Loading your data
      2. Reviewing the data
      3. Refining the data
      4. Creating a prediction
        1. Supply chain prediction
      5. Predictors
      6. Main insights
    4. Reviewing the results
    5. Sharing with a dashboard
      1. Adding a new visualization
    6. Summary
  9. Healthcare Dialoguing
    1. The problem defined
    2. What is dialoguing?
      1. Leveraging (new) data to identify risk
    3. Getting started
      1. Gathering and reviewing data
    4. Building the project
    5. Reviewing the results
      1. Exploring the dialog data
      2. Collecting the data
      3. Moving on
    6. Recap
    7. Results
      1. Data quality of the prediction
      2. Data quality report
      3. More predictive strength
      4. More detail
      5. Assembling a story
    8. Testing your story
    9. Summary
  10. Social Media Sentiment Analysis
    1. The problem defined
    2. Social media and IBM Watson Analytics
      1. Getting started
      2. Creating a Watson Analytics social media project
      3. Building the project
      4. Project creation step by step
      5. Adding topics
      6. Social media investigative themes
    3. Adding dates
      1. Languages
      2. Sources
      3. Reviewing the results
    4. Deeper dive – conversation clusters
      1. Navigation
      2. Topics
    5. Another look
    6. Sentiment
      1. Sentiment terms
      2. Geography
      3. Sources and sites
      4. Influential authors
    7. Author interests
      1. Games and shopping 
    8. Behavior
      1. Demographics
      2. The sentiment dictionary
    9. The data
    10. Summary
  11. Pattern Recognition and Classification
    1. The problem defined
    2. Data peeking
    3. Starting a pattern recognition and classification project
      1. Investigation
      2. Coach me
    4. More with Watson Analytics
      1. The insight bar
      2. Modifying a visualization
      3. Additional filtering
      4. Item-based calculations
      5. Navigate
        1. Compare
        2. Simply trending
    5. Developing the pattern recognition and classification project
      1. Quality
      2. The Watson Analytics data quality report
    6. Creating the prediction
      1. The prediction workflow
        1. Understanding the workflow step by step
    7. Reviewing the results
    8. Displaying top predictors and predictive strength
    9. Summary
  12. Retail and Personalized Recommendations
    1. The problem defined
      1. Product recommendation engines
      2. Recommendations from Watson Analytics
      3. The data at a glance
    2. Starting the project
      1. Range filter
      2. Save me
    3. Developing the project
    4. Reviewing the results
      1. Targets
    5. Summary ribbon
    6. The top predictors
    7. Sharing the insights
    8. Summary
  13. Integration for Sales Forecasting
    1. The problem defined
      1. Product forecasting
      2. Systematic forecasting
    2. IBM Planning Analytics
      1. Our data
      2. Creating the forecast
      3. Starting the project
      4. Developing the project
      5. Visualizations and data requirements
      6. More questioning
    3. Time Series
    4. Other visualization options
    5. Reviewing the results
    6. Summary
  14. Anomaly Detection in Banking Using AI
    1. Defining the problem
    2. Banking use cases
      1. Corruption
      2. Cash
      3. Billing
      4. Check tampering
      5. Skimming
      6. Larceny
      7. Financial statement fraud
    3. Starting the project
      1. The data
    4. Developing the project
      1. The first question
      2. Using Excel for sorting and filtering the data
      3. Back to Watson
      4. Check numbers
    5. Reviewing the results
      1. Collecting
      2. Telling the story
    6. Summary
  15. What's Next
    1. Chapter-by-chapter summary
      1. Chapter 1 – The Essentials of IBM Watson
      2. Chapter 2 – A Basic Watson Project
      3. Chapter 3 – An Automated Supply Chain Scenario
      4. Chapter 4 – Healthcare Dialoguing
      5. Chapter 5 – Social Media Sentiment Analysis
      6. Chapter 6 – Pattern Recognition And Classification
      7. Chapter 7 – Retail And Personalized Recommendations
      8. Chapter 8 – Integration for Sales Forecasting
      9. Chapter 9 – Anomaly Detection in Banking With AI
    2. Suggested next steps
      1. Packt Publishing books, blogs, and video courses
      2. Learning IBM Watson Analytics
      3. LinkedIn groups
      4. Product documentation
        1. IBM websites
        2. Experiment
    3. Summary
  16. Other Books You May Enjoy
    1. Leave a review - let other readers know what you think