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

This course presents advanced models available in IBM SPSS Modeler. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core factors, referred to as components or factors. The next topics focus on supervised models, including Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed on how to analyze text data, combine individual models into a single model, and how to enhance the power of IBM SPSS Modeler by adding external models, developed in Python or R, to the Modeling palette.

This course is designed to teach students core concepts of IBM Watson Explorer Deep Analytics Edition oneWEX. Students will learn to identify the oneWEX platforms as well as the process flow and data flow of oneWEX projects. Students will explore oneWEX tools, such as Content Miner and the Admin Console, while gaining hands-on experience in data acquisition and enrichment. Finally, students will be exposed to more advanced topics, such as Application Builder, Content Analytics Studio, and API usage.

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

This course is designed to teach students core concepts of IBM Watson Explorer Deep Analytics Edition oneWEX. Students will learn to identify the oneWEX platforms as well as the process flow and data flow of oneWEX projects. Students will explore oneWEX tools, such as Content Miner and the Admin Console, while gaining hands-on experience in data acquisition and enrichment. Finally, students will be exposed to more advanced topics, such as Application Builder, Content Analytics Studio, and API usage.

This course provides an introduction to supervised models, unsupervised models, and association models. This is an application-oriented course and examples include predicting whether customers cancel their subscription, predicting property values, segment customers based on usage, and market basket analysis.

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

This course provides an introduction to supervised models, unsupervised models, and association models. This is an application-oriented course and examples include predicting whether customers cancel their subscription, predicting property values, segment customers based on usage, and market basket analysis.

In this course, you will learn the core features and functionality of Watson Explorer Analytical Components. This functionality is found in Watson Explorer Advanced Edition. The Advanced Edition also includes the Foundational Components functionality which is covered in a separate 4 day course (O3100).

This course is designed to introduce the technical student to using the content analytics, annotator, and content mining functionality. This primary functionality is found in an Analytics collections crawling, parsing, indexing, annotating, and searching components. In this course we will look at the results of analytic collections thru the products Content Miner application. This course will also look at creating and deploying a custom annotator using Content Analytics Studio.

This course offers hands-on labs giving students exposure to the various aspects of configuring analytics collections and creating custom annotators that can be used in solutions that will involve Watson Explorer Analytical Components functionality.

In this course, you will learn the core features and functionality of Watson Explorer Foundational Components. This core functionality is found in all editions of Watson Explorer (Enterprise and Advanced edition).

This course is designed to introduce the technical student to using the enterprise search functionality in creating applications using the Search Engine and Application Builder capabilities. In this course will look at configuring search collection and engine components for ingesting, converting, indexing, and querying. This course will also introduce you to the design process of creating an Application Builder 360-degree application.

This course offers hands-on labs giving students exposure to the various aspects of configuring components that will be used in solutions that will involve Watson Explorer Foundational Components functionality.