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Ever get a little confused by these three terms that sound so similar? Let's get some clarity on these important concepts.
Learn how EvalML leverages Woodwork, Featuretools and the nlp-primitives library to process text data and create a machine learning model that can detect spam text messages.
Go on a guided tour of how EvalML automatically builds, optimizes and evaluates supervised machine learning pipelines.
Let's explore how Featuretools generates new features for use in machine learning — automatically, quickly and easily.
Introducing EvalML — an open-source library for automated machine learning (AutoML) and model understanding, written in Python.
Knowing which customers might churn is helpful, but uplift modeling can give you a new window into the nuances of customers' responses, among other applications.
How to build better training examples in a fraction of the time.
I'll give a shoutout in the subsequent Tackling Competitions Post to the person with the best score in the previous post as an added incentive!
To make it easier to understand how a feature was generated automatically, Featuretools now has the ability to graph the lineage of a feature.
Never made an analytical model, or don't have enough time to dedicate to learning statistics, data science, and programming... but you know the business and have questions to answ...
It's tedious to capture and input data by hand. Instead, capture data in image form and extract the data from those images.
Want to bring the power of Python to your data visualization -- all within Designer? Learn how to use the Python Tool to create charts, graphs and plots.