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Important concepts and terminologies -- Structured versus unstructured -- Text analytics -- Searching -- 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
Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services Key FeaturesImplement data science and machine learning
IBM® WatsonTM Content Analytics (Content Analytics) Version 3.0 (formerly known as IBM Content Analytics with Enterprise Search (ICAwES)) helps you to unlock t
Utilize Python and IBM Watson to put real-life use cases into production. KEY FEATURES ● Use of popular Python packages for building Machine Learning solution
The Building Cognitive Applications with IBM Watson Services series is a seven-volume collection that introduces IBM® WatsonTM cognitive computing services. Th
