Knowing who trained the model, how, and with what data are all necessary before believing in any results.
Think ahead to production so that you don’t let your machine learning project collapse before it even gets started.
Get the most out of your machine with these techniques.
Do you need to improve your regression model’s performance? Use this sample Python code to help you find residuals in your own projects.
To do any data science of value we need models that accurately represent our data set. Here’s how to evaluate a model’s fit to your training data.
Games and gaming offer a useful analogy for real life. By closely examining the way AI plays games, we can learn some valuable lessons.
Here are a few important caveats to keep in mind when you’re encoding data with pandas.get_dummies().
Here’s an overview of some commonly used Python libraries that provide an easy and intuitive way to transform images.
In honor of #BlackinDataWeek, the Sadie Collective has a list of nine Black women data scientists to know.
Google’s LaMDA is making people believe that it’s a person with human emotions. It’s probably lying, but we need to prepare for a future when AI might, in fact, be sentient.
