Unsupervised machine learning explores data to find new patterns without set goals. It fuels advancements in tech fields like autonomous driving and content recommendations. Investors can use ...

But machine learning comes in many different flavors. In this post, we will explore supervised and unsupervised learning, the two main categories of machine learning algorithms.

Unsupervised learning is a type of machine learning algorithm that is becoming more popular as the amount of data being produced continues to increase.

Here are the differences between supervised, semi-supervised, and unsupervised learning -- and how each is valuable in the enterprise.

A very quick note on machine learning Before we dive into supervised and unsupervised learning, let’s have a zoomed-out overview of what machine learning is.

Unsupervised learning is a powerful type of machine learning where algorithms analyse and find patterns in data without any human intervention or prior knowledge of categories.

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With unsupervised machine learning, the algorithm needs no knowledge of the physical layout of the machine or its mechanical processes. In fact, the algorithm is agnostic to machine and sensor type.

So instead of fearing machine learning, organizations should learn how to use the technology to the best advantage while also understanding its limitations.

Unsupervised Learning happens when the machine is fed with random data sets that are not labeled, and not in order. The machine has to figure out how to produce the results.