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Machine Learning algorithms are generally categorized based upon the type of output variable and the type of problem that needs to be addressed. These algorithms are broadly divided into three types i.e. Regression, Clustering, and Classification. Regression and Classification are types of supervised learning algorithms while Clustering is a type of unsupervised algorithm. Read more on this link
Recommender systems are the systems that are designed to recommend things to the utilizer predicated on many different factors. These systems presage the most likely product that the users are most liable to purchase and are of interest to. Companies like Netflix, Amazon, etc. utilize recommender systems to avail their users to identify the correct product or movies for them.Read more on this link
Clustering is an example of an unsupervised learning algorithm, in contrast to regression and classification, which are both examples of supervised learning algorithms. Data may be labeled via the process of classification, while instances of similar data can be grouped together through the process of clustering. Read more on the link
Consider a library where books belonging to the same subject are grouped. For instance, all historical books are kept together; all related to science are grouped, and so on. Now imagine that the library is your data analysis project, and data is grouped based on some features (gender, location, data type, etc.)-this is called classification.Read more on the link
As AI becomes more widespread in ecommerce, two topics many are interested in understanding are clustering and classification. In a nutshell, these techniques use machine learning (ML) to group information. But the devil is in the details-while they do share a common goal, we can’t overlook their major differences.Read more on the link
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