Artificial intelligence (AI) is an area that focuses on enabling machines and software to process information and make decisions autonomously. Machine learning, a component of AI, involves computer systems enhancing their problem-solving and comprehension of complex issues through automated techniques.
The three central machine-learning methodologies that programmers can use are supervised learning, unsupervised learning, and reinforcement learning. For in-depth information on supervised machine learning and reinforcement machine learning, kindly refer to the articles dedicated to them. Here you can read up on the basics of unsupervised machine learning.
What is unsupervised machine learning?
With unsupervised machine learning, a system is like a curious toddler exploring a world they know nothing about. The system is exploring data without knowing what it's looking for but is excited -- in a digital kind of way -- about any new pattern it stumbles upon.
With this type of machine learning, algorithms sift through heaps of unstructured data without any specific directions or end goals in mind. They are looking for previously unknown patterns, much as you might look for a new stock pick in an overlooked corner of the market. This is rarely the last step since the owner of the raw data typically applies more sophisticated deep learning or supervised machine learning analyses to any potentially interesting patterns.
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