Generative AI is only as powerful as the data it holds - and public models aren’t likely to hold valuable data you’ve collected over years of running campaigns.
Work with us to understand how data operates in LLMs and take steps to ensure you fill a private model with quality data. Organising it into a generalized model will allow you to generate responses that are just for you - based on you.
This half-day lab focuses on generating responses that are highly relevant to your organization by using your existing data. We’ll start by covering some data basics before we discuss preparing data for machine learning. Then we’ll look at what unbiased data looks like, as well as feature engineering.
We’ll finish the session by giving you guidance on how to communicate with data scientists and engineers so you can confidently express your needs and work together harmoniously.
This Data Literacy for AI course is available as a private training session that can be delivered via Virtual Classroom, or at a location of your choice in the US.
Course overview
Who should attend:
This course is ideal for SMB owners performing their own marketing, senior marketing professionals, and senior business leaders, such as those working in sales.
What you'll learn:
By the end of this course, you should be able to:
- Describe fundamental data concepts
- Describe data classifications for Machine Learning
- Prepare and modify your data
- Work confidently with data scientists and engineers
- Communicate the purpose of powering tools with your data
Prerequisites
In order to get the most out of this session, you'll need the knowledge and skills covered in our GenAI for Marketers: Fundamentals course.
Course agenda
- Data fundamentals
- Data that powers Machine Learning
- Data classification
- How to prep your data
- How to make changes to data
- Feature engineering
- Tools, information and strategies
- Turning ideas into solutions
- Common miscommunications and pain points
Next Steps:
Consider taking the following courses to further your GenAI knowledge: