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IBM Watson Projects
James Miller 著
更新時間：2021-07-16 17:31:59
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IBMWatsonprovidesfast,intelligentinsightinwaysthatthehumanbrainsimplycan'tmatch.Througheightvariedprojects,thisbookwillhelpyouexplorethecomputingandanalyticalcapabilitiesofIBMWatson.ThebookbeginsbyrefreshingyourknowledgeofIBMWatson'sbasicdatapreparationcapabilities,suchasaddingandexploringdatatoprepareitforbeingappliedtomodels.Theprojectscoveredinthisbookcanbedevelopedfordifferentindustries,includingbanking,healthcare,media,andsecurity.TheseprojectswillenableyoutodevelopanAImindsetandguideyouindevelopingsmartdata-drivenprojects,includingautomatingsupplychains,analyzingsentimentinsocialmediadatasets,anddevelopingpersonalizedrecommendations.Bytheendofthisbook,you'llhavelearnedhowtodevelopsolutionsforprocessautomation,andyou'llbeabletomakebetterdata-drivendecisionstodeliveranexcellentcustomerexperience.
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- Other Books You May Enjoy
- Summary
- Experiment
- IBM websites
- Product documentation
品牌：中圖公司
上架時間：2021-07-16 17:28:39
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Leave a review - let other readers know what you think 更新時間：2021-07-16 17:31:59
- Other Books You May Enjoy
- Summary
- Experiment
- IBM websites
- Product documentation
- LinkedIn groups
- Learning IBM Watson Analytics
- Packt Publishing books blogs and video courses
- Suggested next steps
- Chapter 9 – Anomaly Detection in Banking With AI
- Chapter 8 – Integration for Sales Forecasting
- Chapter 7 – Retail And Personalized Recommendations
- Chapter 6 – Pattern Recognition And Classification
- Chapter 5 – Social Media Sentiment Analysis
- Chapter 4 – Healthcare Dialoguing
- Chapter 3 – An Automated Supply Chain Scenario
- Chapter 2 – A Basic Watson Project
- Chapter 1 – The Essentials of IBM Watson
- Chapter-by-chapter summary
- What's Next
- Summary
- Telling the story
- Collecting
- Reviewing the results
- Check numbers
- Back to Watson
- Using Excel for sorting and filtering the data
- The first question
- Developing the project
- The data
- Starting the project
- Financial statement fraud
- Larceny
- Skimming
- Check tampering
- Billing
- Cash
- Corruption
- Banking use cases
- Defining the problem
- Anomaly Detection in Banking Using AI
- Summary
- Reviewing the results
- Other visualization options
- Time Series
- More questioning
- Visualizations and data requirements
- Developing the project
- Starting the project
- Creating the forecast
- Our data
- IBM Planning Analytics
- Systematic forecasting
- Product forecasting
- The problem defined
- Integration for Sales Forecasting
- Summary
- Sharing the insights
- The top predictors
- Summary ribbon
- Targets
- Reviewing the results
- Developing the project
- Save me
- Range filter
- Starting the project
- The data at a glance
- Recommendations from Watson Analytics
- Product recommendation engines
- The problem defined
- Retail and Personalized Recommendations
- Summary
- Displaying top predictors and predictive strength
- Reviewing the results
- Understanding the workflow step by step
- The prediction workflow
- Creating the prediction
- The Watson Analytics data quality report
- Quality
- Developing the pattern recognition and classification project
- Simply trending
- Compare
- Navigate
- Item-based calculations
- Additional filtering
- Modifying a visualization
- The insight bar
- More with Watson Analytics
- Coach me
- Investigation
- Starting a pattern recognition and classification project
- Data peeking
- The problem defined
- Pattern Recognition and Classification
- Summary
- The data
- The sentiment dictionary
- Demographics
- Behavior
- Games and shopping
- Author interests
- Influential authors
- Sources and sites
- Geography
- Sentiment terms
- Sentiment
- Another look
- Topics
- Navigation
- Deeper dive – conversation clusters
- Reviewing the results
- Sources
- Languages
- Adding dates
- Social media investigative themes
- Adding topics
- Project creation step by step
- Building the project
- Creating a Watson Analytics social media project
- Getting started
- Social media and IBM Watson Analytics
- The problem defined
- Social Media Sentiment Analysis
- Summary
- Testing your story
- Assembling a story
- More detail
- More predictive strength
- Data quality report
- Data quality of the prediction
- Results
- Recap
- Moving on
- Collecting the data
- Exploring the dialog data
- Reviewing the results
- Building the project
- Gathering and reviewing data
- Getting started
- Leveraging (new) data to identify risk
- What is dialoguing?
- The problem defined
- Healthcare Dialoguing
- Summary
- Adding a new visualization
- Sharing with a dashboard
- Reviewing the results
- Main insights
- Predictors
- Supply chain prediction
- Creating a prediction
- Refining the data
- Reviewing the data
- Loading your data
- Building the Watson project
- Gathering and reviewing data
- Getting started
- The problem defined
- An Automated Supply Chain Scenario
- Summary
- Reviewing the results
- An insight
- Details page
- Main Insight page
- Top predictors
- Creating a prediction
- Refine or Explore
- Improving your score with Refine
- What does this mean?
- Data review
- Loading your data
- Building your Watson project
- Gathering data
- Getting started
- The problem defined
- A Basic Watson Project
- Summary
- Refine
- Add – some data
- Saving the original
- Refine
- Social media
- Assemble
- Predict
- Watson prompts
- Explore
- The first step
- Basic tasks refresher
- Content panel area
- Search add filter and sort
- Quick start information bar
- Menu bar
- The Watson dashboard
- What about Watson?
- Online glossary let's chat and feedback
- Profile – avatar
- Manage
- Support
- Docs
- Catalog
- IBM Cloud
- Menu icon
- The menu bar
- Exploring the Watson interface
- IBM Cloud prerequisites
- Definition and objectives
- The Essentials of IBM Watson
- Reviews
- Get in touch
- Conventions used
- Download the example code files
- To get the most out of this book
- What this book covers
- Who this book is for
- Preface
- Packt is searching for authors like you
- About the reviewer
- About the author
- Contributors
- Packt.com
- Why subscribe?
- Packt Upsell
- IBM Watson Projects
- Copyright and Credits
- Title Page
- 封面
- 封面
- Title Page
- Copyright and Credits
- IBM Watson Projects
- Packt Upsell
- Why subscribe?
- Packt.com
- Contributors
- About the author
- About the reviewer
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Conventions used
- Get in touch
- Reviews
- The Essentials of IBM Watson
- Definition and objectives
- IBM Cloud prerequisites
- Exploring the Watson interface
- The menu bar
- Menu icon
- IBM Cloud
- Catalog
- Docs
- Support
- Manage
- Profile – avatar
- Online glossary let's chat and feedback
- What about Watson?
- The Watson dashboard
- Menu bar
- Quick start information bar
- Search add filter and sort
- Content panel area
- Basic tasks refresher
- The first step
- Explore
- Watson prompts
- Predict
- Assemble
- Social media
- Refine
- Saving the original
- Add – some data
- Refine
- Summary
- A Basic Watson Project
- The problem defined
- Getting started
- Gathering data
- Building your Watson project
- Loading your data
- Data review
- What does this mean?
- Improving your score with Refine
- Refine or Explore
- Creating a prediction
- Top predictors
- Main Insight page
- Details page
- An insight
- Reviewing the results
- Summary
- An Automated Supply Chain Scenario
- The problem defined
- Getting started
- Gathering and reviewing data
- Building the Watson project
- Loading your data
- Reviewing the data
- Refining the data
- Creating a prediction
- Supply chain prediction
- Predictors
- Main insights
- Reviewing the results
- Sharing with a dashboard
- Adding a new visualization
- Summary
- Healthcare Dialoguing
- The problem defined
- What is dialoguing?
- Leveraging (new) data to identify risk
- Getting started
- Gathering and reviewing data
- Building the project
- Reviewing the results
- Exploring the dialog data
- Collecting the data
- Moving on
- Recap
- Results
- Data quality of the prediction
- Data quality report
- More predictive strength
- More detail
- Assembling a story
- Testing your story
- Summary
- Social Media Sentiment Analysis
- The problem defined
- Social media and IBM Watson Analytics
- Getting started
- Creating a Watson Analytics social media project
- Building the project
- Project creation step by step
- Adding topics
- Social media investigative themes
- Adding dates
- Languages
- Sources
- Reviewing the results
- Deeper dive – conversation clusters
- Navigation
- Topics
- Another look
- Sentiment
- Sentiment terms
- Geography
- Sources and sites
- Influential authors
- Author interests
- Games and shopping
- Behavior
- Demographics
- The sentiment dictionary
- The data
- Summary
- Pattern Recognition and Classification
- The problem defined
- Data peeking
- Starting a pattern recognition and classification project
- Investigation
- Coach me
- More with Watson Analytics
- The insight bar
- Modifying a visualization
- Additional filtering
- Item-based calculations
- Navigate
- Compare
- Simply trending
- Developing the pattern recognition and classification project
- Quality
- The Watson Analytics data quality report
- Creating the prediction
- The prediction workflow
- Understanding the workflow step by step
- Reviewing the results
- Displaying top predictors and predictive strength
- Summary
- Retail and Personalized Recommendations
- The problem defined
- Product recommendation engines
- Recommendations from Watson Analytics
- The data at a glance
- Starting the project
- Range filter
- Save me
- Developing the project
- Reviewing the results
- Targets
- Summary ribbon
- The top predictors
- Sharing the insights
- Summary
- Integration for Sales Forecasting
- The problem defined
- Product forecasting
- Systematic forecasting
- IBM Planning Analytics
- Our data
- Creating the forecast
- Starting the project
- Developing the project
- Visualizations and data requirements
- More questioning
- Time Series
- Other visualization options
- Reviewing the results
- Summary
- Anomaly Detection in Banking Using AI
- Defining the problem
- Banking use cases
- Corruption
- Cash
- Billing
- Check tampering
- Skimming
- Larceny
- Financial statement fraud
- Starting the project
- The data
- Developing the project
- The first question
- Using Excel for sorting and filtering the data
- Back to Watson
- Check numbers
- Reviewing the results
- Collecting
- Telling the story
- Summary
- What's Next
- Chapter-by-chapter summary
- Chapter 1 – The Essentials of IBM Watson
- Chapter 2 – A Basic Watson Project
- Chapter 3 – An Automated Supply Chain Scenario
- Chapter 4 – Healthcare Dialoguing
- Chapter 5 – Social Media Sentiment Analysis
- Chapter 6 – Pattern Recognition And Classification
- Chapter 7 – Retail And Personalized Recommendations
- Chapter 8 – Integration for Sales Forecasting
- Chapter 9 – Anomaly Detection in Banking With AI
- Suggested next steps
- Packt Publishing books blogs and video courses
- Learning IBM Watson Analytics
- LinkedIn groups
- Product documentation
- IBM websites
- Experiment
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-07-16 17:31:59
