In the first article in this series, we highlighted some of the motivations for using artificial intelligence in the enterprise to better understand the present and predict the future.1 In the second article, we provided descriptions of data analytics, data science, machine learning and deep learning.2 In this article, we continue with a practical description of machine learning and, in our follow-on article, we will do the same for deep learning, a subset of machine learning.
What is machine learning?
Machine learning is one approach to AI — not the only approach, but currently one that is easily the most successful in enterprise applications and more. Machine learning approaches in AI are different from explicit, rules-based AI approaches, such as expert systems, in that they are designed to learn from the data. The algorithms at the heart of machine learning applications use data to generate and refine rules (as opposed to the programmer explicitly defining the rules). The computer then decides how to respond based on what it has learned from the data. READ MORE ON: CIO