This guide introduces you to a suite of classification performance metrics in Python and some visualization methods that every data scientist should know.
Data scientists need to have a good understanding of how to select the best features when it comes to model building. This guide will help you get started.
To make the most of machine learning for their clients, data scientists need to be able to explain the likely factors behind a model's predictions. Python offers multiple ways to do just that.
It’s cutting-edge now, but soon a data fabric will be an essential tool. Here’s how it will transform data architecture and create a new competitive advantage.
By leveraging natural language processing, augmented analytics could revolutionize the way data science teams — and non-specialist business users — get the information their firms need.
As a data scientist, you may be able to get away without using linear algebra — but not for long. Here’s how linear algebra can improve your machine learning, computer vision and natural language processing.
Hiring demand for experts in artificial intelligence, machine learning, and deep learning is high, and will only get higher in coming years. Here’s how to make sure you get the best of the best.
