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Machine Learning is the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. An example of this is Spotify using what you listen to to recommend songs to you

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There are 2 types of filtering methods. One is Collaborative and the other is Content-Based. Collaborative filtering is when one person's content influences the others content. For Example, If there is a shared account on Netflix and one person is watching a show, it is going to recommend that show to the other person.

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We all opened Colab and listened to 23 songs as a class. We rated each of them 1-10. We also picked 3 attributes about a song that we liked. So, for me, I picked Liveness, Loudness and speechiness. These are all the things I take into consideration when I listen to a song. Once we rated everyone, we were given a string that we copied and pasted into a google form that was then transferred into a google sheet.

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On Assignment 7.4, after running the code and importing our rating string, we were able to see a table of all the songs and what we rated them. We then got to see what our 3 attributes looked like as graphs. WE also learned about training and testing points. We did an activity where we measured the wingspan and height of 3 people in our group. We made a table and put the table into Desmos and got a graph. We also used a lot of equations to see which graph fit the data best. Levels of complexity are the different types of graph. When you just the slider on Colab, you get different types of graphs with different errors, and you want the lowest error.

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I don't know what song it is so idk if it is right.

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I have similar music taste to Lily,Caden and Alexis for favorite songs.

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Machine Learning si all about using what the user watches or listens to to recommend similar content to them. I don't know what bias could be used, I do know that you could be biased because of the genre of song.