“Data scientist” is more difficult to define than terms used to describe other scientists, such as chemist, biologist, geneticist, or meteorologist. Part of the problem may be due to the fact that “data science” became a commonly used term long before data science became a formal field of study. Even now people who call themselves […]
Supervised vs unsupervised learning models have different use cases with machine learning. The key difference is whether you're working with labeled or unlabeled data.
Data science teams capture, store, and analyze data to extract valuable information and insight. In recent posts, I focused on capturing and storing three types of data — structured, semi-structured, and unstructured — and I encouraged readers to avoid the common trap of allowing big data to become big garbage. In this article, I shift […]
An artificial neural network is a type of machine learning that takes input and recognizes patterns then makes a decision or predication about the output.
A database schema is the organization of data in a database. They are usually found in a relational database management system.
Right now we focus on capacity, but we need to teach machines right from wrong. This is a difficult data ethics challenge in artificial intelligence. How to build a moral machine?
