Before marketers commit to and execute their AI strategy, they need to understand the opportunity and difference between data analytics, predictive analytics and AI machine learning.

In my last article I wrote about the difference between AI and Machine Learning (ML). While ML is often described as a sub-discipline of AI, ... by “training” itself on the new data it receives.

Artificial intelligence, machine learning, and deep learning have become integral for many businesses. But, the terms are often used interchangeably. Here's how to tell them apart.

Machine learning relies on huge amounts of “training data.” Such data is often compiled by humans via data labeling (many of those humans are not paid very well ).

Machine learning algorithms are often divided into supervised (the training data are tagged with the answers) and unsupervised (any labels that may exist are not shown to the training algorithm).

Artificial intelligence is inescapable nowadays. There’s generative AI to create an ad and AI platforms to manage campaigns. Your refrigerator and maybe even your toothbrush have AI embedded in them.

In histopathology, where tissues are studied under the microscope to understand and diagnose diseases, stains represent a ...

Quality data is at the heart of the success of enterprise artificial intelligence (AI). And accordingly, it remains the main source of challenges for companies that want to apply machine learning ...

In practice, exploratory data analysis combines graphics and descriptive statistics. In a highly cited book chapter, Tukey uses R to explore the 1990s Vietnamese economy with histograms, kernel ...

Data poisoning is a type of attack that involves tampering with and polluting a machine learning model's training data, impacting the model's ability to produce accurate predictions.