AI/ML

Let’s take a look at the different steps to build a prediction model and go over the what, when, why, and how people accomplish them.

Many invest heavily into remarketing strategies with vendors, but what about building your own? Leverage existing consumer data for your next campaign.

Apply feature engineering by converting time series data to numerical values for training machine learning models.

When collecting consumer data, there are times when you’re unable to retrieve all the data so you’ll want to “guestimate” what the data should be.

Machines have an easy time understanding numbers, but can't comprehend meaning. Learn techniques to scale numerical data to better grasp your data.

With the evolution of AI comes many tools for product developers. AI isn’t replacing your job, it’s making it easier and here’s how.

The future of business means AI. Onboarding talent capable of collecting data via machine learning is expensive but essential to stay relevant.

Machines have a hard time understanding data humans can read. Apply encoding on qualitative data to train models more efficiently.

A machine learning (ML) project requires collaboration across many roles. Here are high level steps in the end-to-end ML lifecycle for each role they play.

As a step towards data processing, learn how to merge multiple datasets together and analyze the story behind the data.