Course

Re:infer Overview

Can companies use business emails and chats to get insights and identify automation opportunities? Yes, it’s called communications mining and UiPath now offers the perfect tool for this: Re:infer.
  • 2628 enrolled students
  • December 14, 2022
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Re:infer Overview

  • 01:00:00
  • Downloadable resources available
  • Diploma of Completion included
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Course details

Difficulty Level

Beginner

Language

English

Completion time

01:00:00

Product covered

Re:infer

About the Re:infer Overview course 

This introductory course is meant to provide technical audiences and business users with an understanding of what Re:infer is, what it does, how it works, and how it helps companies to process vast amounts of unstructured communications data, such as emails, instant messages, log notes, and so on. 

 

This course will take you through the process of turning unstructured communication data into structured data and processing it using Natural Language Processing (NLP) to generate business intelligence, valuable analytics, and automation opportunities.  

 

The Re:infer overview is an intermediate course. It takes 1 hour to complete. At the end, you’ll receive a diploma of completion. 

 

Course prerequisites 

This course is designed for people completely new to Re:infer. Thus, it doesn't have any pre-requisites. 

 

Course audience 

The Re:infer Overview course is aimed at technical audiences and business users, as well as anybody else curious to see the power of Natural Language Processing in business. 

 

The Re:infer course agenda 

We’ll introduce Re:infer at an overview level before looking closely at some of its main capabilities. The full agenda covers:  

  • Getting started with Re:infer 
  • Introduction to Re:infer 
  • How does Re:infer Work? 
  • What does Re:infer look like? 

 

Re:infer Overview learning Objectives 

At the end of the Re:infer Overview course, you should be able to: 

  • Define the main concepts related to Re:infer. 
  • Recognize the types of problems that can be solved with the help of Re:infer. 
  • Describe how Re:infer works and the types of data it can manage. 
  • Describe the key characteristics of Re:infer models. 
  • Identify the tools that are integrated downstream with Re:infer. 
  • Describe the main features of the Re:infer Platform.