Don't forget to set a section icon and cover page (if applicable)
For the copyright notice, make sure to:
Link to the MIT License of this course's GitHub repository. By default, the MIT License links to the Java course repo.
Update the copyright year
(Delete the to do list once you're done.)
Something you may have noticed about all of the previous sections is the need for data to run the models we described. Most problems involve using data in order to predict something new, however there are also models that do not need data to work. This is called unsupervised learning. As opposed to the supervised learning of past models, it doesn’t require human “supervision” by providing data. In this section you will learn to apply unsupervised models to solve problems without prior data.
The problem we will be going through for this section has to do with detecting anomalies. For example, say you were given a dataset but in your data exploration phase you see outliers or data that just doesn’t fit. Through an unsupervised model we can sort these outliers out to clean our dataset and ignore the human error possible when making datasets. Before we can apply this we need to first understand how the model will see the data.
You can add practice problems here if you want. Follow the format below.
Create a new class called
AboutMe. In the
mainmethod, print your name. On the same line, print any message. On the next line(s), print a stanza from your favorite poem. Make sure it's formatted correctly. And of course, be sure to have good programming style!
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