Course

Fundamentals of Model Training

Tired of spending hours sifting through business emails? Let our labels and entities take the hassle out of your inbox!
  • 9459 enrolled students
  • October 25, 2023
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Fundamentals of Model Training

  • 00:30:00
  • Downloadable resources available
  • Diploma of Completion included
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Course details

Difficulty Level

Beginner

Language

English

Completion time

00:30:00

Product covered

UiPath Communications Mining

About the Fundamentals of Model Training course 

This course is meant to provide technical audiences and business users with the knowledge required before training a model on their data in the UiPath Communications Mining platform. 

This course will go through key concepts, including labels and entities and how they work, as well as an overview of the steps involved in end-to-end model training process. 

The Fundamentals of Model Training takes 30 mins to complete. At the end, you’ll receive a diploma of completion. 

 

Course prerequisites 

To make the most of this learning experience, we recommend going through the introductory UiPath Communications Mining Overview course. 

 

Course audience 

The Fundamentals of Model Training 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 Fundamentals of Model Training course agenda 

The full agenda covers:  

  • What is model training and what makes a ‘good’ model 
  • Label, entities, and metadata 
  • Introduction to model training 
  • Training with label sentiment analysis 

 

Fundamentals of Model Training course learning objectives 

At the end of the Fundamentals of Model Training course, you should be able to: 

  • Define what model training is. 
  • Describe what makes a 'good' model. 
  • Explain labels, entities, metadata, and how to use them. 
  • Explain what the model learns from during training. 
  • Explain the overview of model training process. 
  • Explain best practices for applying labels and entities.