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Introduction to IBM SPSS Modeler Text Analytics (v18.1.1) SPVC

Course content updated by LearnQuest
Price
875 - 1,630 USD
16 Hours
0E108GS
Self-Paced Training
IBM Business Partner
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  • Learn on Demand
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 875 USD
  • Date: 31-Jan-2022 to 1-Feb-2022
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 1,630 USD
  • Date: 14-Feb-2022 to 15-Feb-2022
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 1,630 USD
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Course Description

Overview

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

This course (formerly: Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (v18)) teaches you how to analyze text data using IBM SPSS Modeler Text Analytics. You will be introduced to the complete set of steps involved in working with text data, from reading the text data to creating the final categories for additional analysis. After the final model has been created, there is an example of how to apply the model to perform churn analysis in telecommunications. Topics include how to automatically and manually create and modify categories, how to edit synonym, type, and exclude dictionaries, and how to perform Text Link Analysis and Cluster Analysis with text data. Also included are examples of how to create resource tempates and Text Analysis packages to share with other projects and other users.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms

Objectives

Please refer to course overview

Audience

Users of IBM SPSS Modeler responsible for building predictive models who want to leverage the full potential of classification models in IBM SPSS Modeler.

Prerequisites

    • General computer literacy
    • Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1.1) is recommended.

Topics

Unit 1 - Introduction to text mining
• Describe text mining and its relationship to data mining
• Explain CRISP-DM methodology as it applies to text mining
• Describe the steps in a text mining project

Unit 2 - An overview of text mining
• Describe the nodes that were specifically developed for text mining
• Complete a typical text mining modeling session

Unit 3 - Reading text data
• Reading text from multiple files
• Reading text from Web Feeds
• Viewing text from documents within Modeler

Unit 4 - Linguistic analysis and text mining
• Describe linguistic analysis
• Describe Templates and Libraries
• Describe the process of text extraction
• Describe Text Analysis Packages
• Describe categorization of terms and concepts

Unit 5 - Creating a text mining concept model
• Develop a text mining concept model
• Score model data
• Compare models based on using different Resource Templates
• Merge the  results with a file containing the customer’s demographics
• Analyze model results

Unit 6 - Reviewing types and concepts in the Interactive Workbench
• Use the Interactive Workbench
• Update the modeling node
• Review extracted concepts

Unit 7 - Editing linguistic resources
• Describe the resource template
• Review dictionaries
• Review libraries
• Manage libraries

Unit 8 - Fine tuning resources
• Review Advanced Resources
• Extracting non-linguistic entities
• Adding fuzzy grouping exceptions
• Forcing a word to take a particular Part of Speech
• Adding non-Linguistic entities

Unit 9 - Performing Text Link Analysis
• Use Text Link Analysis interactively
• Create categories from a pattern
• Use the visualization pane
• Create text link rules
• Use the Text Link Analysis node

Unit 10 - Clustering concepts
• Create Clusters
• Creating categories from cluster concepts
• Fine tuning Cluster Analysis settings

Unit 11 - Categorization techniques
• Describe approaches to categorization
• Use Frequency Based Categorization
• Use Text Analysis Packages to Categorize data
• Import pre-existing categories from a Microsoft Excel file
• Use Automated Categorization with Linguistic-based Techniques

Unit 12 - Creating categories
• Develop categorization strategy
• Fine turning the categories
• Importing pre-existing categories
• Creating a Text Analysis Package
• Assess category overlap
• Using a Text Analysis Package to categorize a new set of data
• Using Linguistic Categorization techniques to Creating Categories

Unit 13 - Managing Linguistic Resources
• Use the Template Editor
• Share Libraries
• Save resource templates
• Share Templates
• Describe local and public libraries
• Backup Resources
• Publishing libraries

Unit 14 - Using text mining models
• Explore text mining models
• Develop a model with quantitative and qualitative data
• Score new data

Appendix A - The process of text mining
• Explain the steps that are involved in performing a text mining project

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Self-Paced Training Info

Learn at your own pace with anytime, anywhere training

  • Same in-demand topics as instructor-led public and private classes.
  • Standalone learning or supplemental reinforcement.
  • e-Learning content varies by course and technology.
  • View the Self-Paced version of this outline and what is included in the SPVC course.
  • Learn more about e-Learning

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Self-Paced Training Terms & Conditions

THIS IS A SELF-PACED VIRTUAL CLASS. AFTER YOU REGISTER, YOU HAVE 30 DAYS TO COMPLETE THE COURSE.

This is a Self-Paced virtual class; it is intended for students who do not need the support of a classroom instructor. If you feel you would better benefit from having access to a Subject Matter Expert, please enroll in the Instructor-Led version instead. Minimal technical support is provided to address issues with accessing the platform or problems within the lab environment.

Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.

After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. A confirmation email will contain your online link, your ID and password, and additional instructions for starting the course.

You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.

If the course requires a remote lab system, the lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system.

Click the Skytap Connectivity Test button to ensure this computer's hardware, software and internet connection works with the SPVC Lab Environment.

Click the Skytap Connectivity Documentation button to read about the hardware, software and internet connection requirements.

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