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A technology type that teaches computers to learn from data and make decisions without exactly being told so. Some examples are:

Email spam filters

Siri/Alexa speech recognition

Recommendation systems

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Content-based filtering recommends things by comparing them to what a user has liked in the past. It looks at different features like genres or keywords.

Collaborative filtering recommends things based on what similar users liked. However, it doesn’t look at its details—just patterns.

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The magic that makes Spotify’s Discover Weekly playlists so damn good

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Thirty songs that feel like a gift from a music-loving friend, but were cooked up by an algorithm.

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Spotify keeps track of the songs you listen to and save, and then finds new songs that sound similar—to create a personalized Discover Weekly for you

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We rated 23 songs to create data for a machine learning model. These ratings help the system learn our music preferences. Then it can recommend new songs each might like using methods like content-based or collaborative filtering.

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1. Danceability

2. Energy

3. Popularity

I chose those three because for me it's important for my song to be energized so people can dance to it. Also, popular, because most popular songs are very good.

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-If the model is too simple it may end up in not learning enough from the data, make poor predictions and miss patterns.

-Too complex models, have too many unnecessary details, works great on training data but fails on new data.

-Models that are just right test the model with new data to make sure it works well in different situations

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This graph gave a rough idea of the song rating. Points don't really line up and while its not bad the graph isn't quite accurate since it didn’t consider other important for me features. Test and train error was high and not close at all, making this model a bad fit.

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This graph was much more accurate. Test and train error was low and close together. It included more features and was a better fit overall.

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My ratings didn't perfectly match with anybody's. Coming at the highest similarity of 83% my ratings aligned with Syd, Landon, Alexis, Amaya, Jada, Lydia, Morgan and Dani

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1. What do your results look like compared to your actual rating?

Some were quite close, very few were off

2. Were there any surprises? Why do you think that happened?

Yes, some predictions were much lower than my actual ratings, but it could be because those songs are personal favorites

3. What does this add to your understanding of collaborative filtering?

It can’t predict personal taste well and works great when many people rate the same things

4. How might you change your model to be more accurate?

Combine together with content based filtering and add more data

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Machine learning is important for people to understand because everything around us uses it everyday. Starting with recommendation systems to medical tools. However, depending on the bias the outcome can change. For example, collaborative filtering may prefer popular items and ignore other preferences and content-based filtering can limit diversity by only recommending similar items

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What public needs to know is that machine learning happens from data, meaning if data is biased, the result will be too. Collaborative filtering often shows popular things and follows what most people like, so it can miss new or different items. Content-based filtering shows similar things based on what you liked before, but it can miss variety if the item info is not complete or fair.