Description
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Course details
| IBM course code: 0A108G | Category: IBM SPSS Modeler / IBM SPSS Modeler |
| Delivery: Online & on-site** | Course length in days: 2 |
Target 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.
Desired Prerequisites:
• General computer literacy
• Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1.1) is recommended.
Instructors
The great majority of the IBM courses we offer are taught directly by our engineers. This is the only way we can guarantee the highest quality. We complement all the training with our own materials and laboratories, based on our experience during the deployments, migrations and courses that we have carried out during all these years.
Added value
Our courses are deeply role oriented. To give an example, the needs for technology mastery are different for developer teams and for the people in charge of deploying and managing the underlying infrastructure. The level of previous experience is also important and we take it very seriously. That is why beyond (boring) commands and tasks, we focus on solving the problems that arise in the day to day of each team. Providing them with the knowledge, competencies and skills required for each project. In addition, our documentation is based on the latest version of each product.
Agenda and course syllabus
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
Do you need to adapt this syllabus to your needs? Are you interested in other courses? Ask us without obligation.
Locations for on-site delivery
- Austria: Vienna
- Belgium: Brussels, Ghent
- Denmark: Cophenhagen
- Estonia: Tallinn
- Finland: Helsinki
- France: Paris, Marseille, Lyon
- Germany: Berlin, Munich, Cologne, Hamburg
- Greece: Athens, Thessaloniki
- Italy: Rome
- Louxemburg: Louxembourg (city)
- Netherlands: Amsterdam
- Norway: Oslo
- Portugal: Lisbon, Braga, Porto, Coimbra
- Slovakia: Bratislava
- Slovenia: Bratislava
- Spain: Madrid, Sevilla, Valencia, Barcelona, Bilbao, Málaga
- Sweden: Stockholm
- Turkey: Ankara
- United Kingdom: London
