Machine learning is defined as an application of artificial intelligence (AI) that looks to automatically learn and improve from experience without being explicitly programmed.
Machine learning is a rapidly growing field that also focuses on the development of computer programs that can access data and use it learn for themselves.
This has many potential benefits for most industries and sectors, including the financial services industry.
Machine Learning Explained
Machine learning can be explained through observational behavior. For example, the process of learning begins with observations or data.
This includes examples and indirect experience or instruction to help detect patterns in data.
In doing so, the goal is to make better decisions in the future based on the examples that are provided.
In an ideal set of circumstances, computers learn automatically without human intervention or assistance and adjust actions accordingly.
Machine learning can take two different form, i.e. supervised or unsupervised learning.
Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.
As such, the system is able to provide targets for any new input after sufficient levels of training.
Learning algorithm can also compare its output to find errors in order to modify the model accordingly.
By extension, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled.
Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.
The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.