Скачать или смотреть Types of Machine Learning Problems - Part 3 | #5 of 28 | Foundations of ML: The Big Picture

In this video, coming to clustering, clustering is a very specific, very special type of machine learning problem, where you try to find groups that are originally existing within the data.

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🔹 Types of Machine Learning Problems and How to Solve them Part: 3


The items that you're trying to grow could be products, it could be people, or it could be anything, you just want to form groups that are inherently existing I suppose if this is your original data set, just by looking at the data points here, you naturally find certain groups that are naturally formed here.

You can use grouping or clustering algorithms to identify these inherent groups. Now, this is very clearly visible, the groups are very clearly visible. But sometimes this might not be very explicit. For example, you might have a very large set of customers in your organization.

And you want to find natural groups of customers, that is customers that are similar to each other, you want to put it in the same group. Whereas customers that are very dissimilar, and have different patterns, it could be buying patterns, it could be demographic factors, the customers that are different from the other set, they want to have it in a different bucket. clustering will help you solve this problem.

Next is recommendation system. Recommendation system is a type of machine learning problem that is concerned about showing products to the users. And those products are something that the users are likely to be interested in. Typically, what is involved is you have a certain set of users in your company, right. And these users are interested in certain products. Let's say this is user 123.

And each of these users are interested in certain number of products. Right now this user is interested in these products, user two might be interested in 345. You know, user three is interested in one and two, you might want to recommend user three, this product because user one or user three have certain amount of commonality between them, the chances that this particular user to be interested in this particular product is a little bit higher, it could be the case, right?

This is one typical example of how a recommendation system can be designed, right? It's quite intuitive, because these two guys are interested in product one, that is customer one and customer three is interested in product one and product two, it might be the case that since there is a good amount of overlap between customer three and customer one, customer three might also be interested in product three, which customer what.

And these are some of the very simple examples of recommendation systems that you would see every day. This is for Netflix.


Let me know in the comments section if you have any questions!


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