A machine learning algorithm isn’t worth much without great training data to power it. After all, algorithms learn from data, discovering relationships, developing understanding, making decisions, and evaluating their confidence from the training data they’re given. And the better the training data is, the better the model performs. In fact, the quality and quantity of your training data has as much to do with the success of your data project as the algorithms themselves.
Join us for this latest Data Science Central webinar on the basics of training data where we will cover:
What training data is and why it’s so important
What training data looks like for a variety of projects
Why training data should be labeled and how to get it labeled
How much training data you need
Speaker: Jennifer Prendki, VP of Machine Learning — Figure Eight
Hosted by: Bill Vorhies, Editorial Director — Data Science Central
