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Coveo Machine Learning

DESCRIPTION

This class will provide you with in-depth information on how to set-up, maintain and analyze Coveo Machine Learning models to significantly improve relevance and success with your Coveo user experience.


By taking this course, you will learn:

  • - Review fundamentals concepts that power the Coveo Platform Machine Learning features
  • - Understand the mechanics the Coveo Machine Learning features
  • - The design principles needed to build a successful Machine Learning driven search experience
  • - How to apply your knowledge to 3 archetypal search experiences
 4 HOURS  250

ELEMENTS COVERED

  • Core concepts of the Coveo Platform that are used by Machine Learning
  • Machine Learning mechanics and flows
  • Design Principles for building a Machine Learning driven search experience
  • How to apply the design principles in the context of a Search portal, an agent Panel and an app exchange.

 IMPACT TRAINING EVENT

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Course Agenda

Day 1

Coveo Core concepts review

Description: In this mandatory step, you will explore the concepts and elements that the Coveo Platform uses to power a Machine Learning driven search experience. This includes Query Parameters, Usage Analytics Events and Dimensions, Context, Query Pipelines and more.

Machine Learning Mechanics

Description: In this section, you will learn about the mechanics through which the Machine Learning features can optimize the experience for your Visitors. You will explore concepts like: how does it learn, what are the key things to know about each model, what type of information should be used to develop the models and more.

Day 2

Design Principles

Description: In this section, you will learn the 4 axis of design when building a Machine Learning driven search experience. You will learn how to clearly define your target audiences in terms that can be leverage to create a Machine Learning driven experience and how to configure your Search Interface to measure your audience behavior and utilization of Machine Learning.

Use Case

Description: In this final section, you will put everything you have learned so far into practice by studying 3 archetypical Machine Learning driven search experiences. We will show how the design principles are applied in the context of:


- A Search Portal

- A agent Panel

- An E-commerce portal

Machine Learning Use Cases

Description: In this section, we will cover the basics of Machine Learning instrumentation in the context of providing relevance. We will analyze a business use case example and discuss the importance of dimensions, custom events and context to make Machine Learning successful.

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