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.
Data privacy is critical to you, but it has never been more challenging to maintain. Applications are spread across on-premises and cloud platforms, including sensitive data that needs to be protected everywhere. How can you protect your data after it leaves the system of record?
â€¢ This course will demonstrate how Data Privacy Passports will provide privacy protection to your environment and assist with your security strength in depth strategy.
â€¢ In this course you will learn how leveraging the Data Centric Audit and Protection (DCAP) capabilities of IBM Data Privacy Passports can help safeguard all sensitive data to comply with data privacy regulations, minimize the amount of sensitive data needlessly shared within the organization and to 3rd parties, ease the burden of manual and cumbersome audits, revoke access to sensitive data, and ultimately have full control over the protection of your data wherever it goes.
This course consists of several independent modules. The modules, including the lab exercises, stand on their own and do not depend on any other content.
Exercises and recorded demos reinforce the concepts and technologies being covered in the lectures.
This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.