Take a peek at why!
Unsupervised machine learning is helping humans dig into heaps of data to find patterns and trends that can improve businesses and keep consumers safe from fraud. These artificially intelligent algorithms are uncovering knowledge in a way that other machine learning methods cannot.
The media update team looks at unsupervised learning algorithms and the interesting ways in which they are being used.
Machine learning, a component of artificial intelligence (AI), is helping humans in many ways, from predicting your best route to work to learning what products to recommend to you.
But we don’t always have complete datasets that machines can learn from. Without comprehensive information, they can’t predict future data or deliver the answers we need.
We might, for instance, not have a dataset of both fraudulent credit card transactions and the attributes that made these incidents fake. In this scenario, a machine wouldn’t be able to learn what the typical characteristics of fake transactions are – and would, therefore, be unable to determine whether new transactions are fraudulent or not.
Enter unsupervised machine learning. Unsupervised machine learning can be defined as a type of machine learning algorithm that can draw conclusions from data that is not classified, categorized or labeled.