Unsupervised Learning in Machine Learning
Simple and short explanation…With an in-depth continuation and links to the wiki…
TLDR:
Unsupervised learning in machine learning is a type of artificial intelligence (AI) that deals with unlabeled data. This means that the data is not labeled or structured in any way, so the algorithm has to extract patterns and insights from the data on its own.
Unsupervised learning is used in a variety of industries, such as finance, healthcare, and retail.
A Good Example of Unsupervised Learning
An example of unsupervised learning is clustering. With clustering, the algorithm groups similar data points together, so that similar items can be identified in the data. For example, in a retail store, clustering can be used to identify customer segments, such as those who buy certain types of items or those who shop at certain times of the day.
What is Clustering?
Clustering is a type of unsupervised learning that groups similar data points together. The algorithm looks for patterns in the data and then groups the data points that have the same characteristics into clusters.
For example, if a retail store is looking to identify customer segments, clustering can be used to group customers into segments based on their buying habits.
This helps the store target its marketing efforts and tailor its products to each customer segment.
Where Unsupervised Learning Can Be Used
Unsupervised learning is used in many applications, such as:
— Image recognition and classification
— Recommender systems
— Anomaly detection
— Fraud detection
— Text mining
If you are interested about any of the topic try to google it or chatgpt it :)
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Review of the Supervised Learning.
And here is my short overview article of basic things about chatGPT.
If you want to understand more about how chatGPTworks — check out this article.
And if you want to get inspiring prompts — here is a compilation of usage of chatGPT.
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