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#7 Machine Learning Specialization [Course 1, Week 1, Lesson 2]

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in the last video you saw what is unsupervised learning and one type of unsupervised learning called clustering let's give a slightly more formal definition of unsupervised learning and take a quick look at some other types of unsupervised learning other than clustering whereas in supervised learning the data comes with both inputs X and output labels Y in unsupervised learning the data comes only with inputs X but not open labels Y and the algorithm has defined some structure or some pattern or something interesting in the data was seeing just one example of unsupervised learning called a clustering algorithm which groups similar data points together in this specialization you learn about clustering as well as two other types of unsupervised learning one is called anomaly detection which is used to detect unusual events this turns out to be really important for fraud detection in the financial system where unusual events unusual transactions could be assigns of Fraud and for many other applications and you also learn about dimensionality reduction this lets you take a big data set and almost magically compress it to a much smaller data set while using as little information as possible in case an army detection and dimensionality reduction don't seem to make too much sense to you yet don't worry about it we'll get to this later in this specialization now I'd like to ask you another question to help you check your understanding and no pressure If you don't get it right on the first try is totally fine please select any of the following that you think are examples of unsupervised learning two are unsupervised examples and two are supervised learning examples so please take a look maybe you remember the spam filtering problem if you have labeled data you know labeled as spam or non-spam email you can treat this as a supervised learning problem the second example the new story example that's exactly the Google news and tandem example that you saw in the last video

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