When people hear “artificial intelligence,” many envision “big data.” There’s a reason for that: some of the most prominent AI breakthroughs in the past decade have relied on enormous data sets. Image ...

Python has become the most popular data science and machine learning programming language. But in order to obtain effective data and results, it’s important that you have a basic understanding of how ...

Join the Drexel Women in Computing Society (WiCS) for a talk with Electrical and Computer Engineering Associate Professor Andrew Cohen, PhD on the use of Kolmogorov complexity and algorithmic ...

What are the differences between econometrics, statistics, and machine learning? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better ...

The most advanced machine learning tools can't replace the humans behind them. Learn how tech-savvy SEO pros can work effectively with automation. The global datasphere will grow from 33 zettabytes ...

The flexibility, agility and ultimate cost of machine learning projects can be significantly impacted by data logistics and dependencies, according to Jim Scott, VP, Enterprise Architecture, at MapR.

Big Blue's Db2 11.5 upgrade adds drivers to artificial intelligence languages as well as natural language queries and visualizations. For IBM, the move to meld the database with data science workflows ...

A multi-institutional research team has demonstrated how AI and machine learning can optimize therapy selection and dosing ...