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In part one of this lesson, you used Google Forms to create a survey that collected ratings from your classmates about items in a certain category.

You added questions and images to your form.

Then, your users rated each item based on how much they like or dislike it.

In part two, you used the data you collected to create a model.

The model analyzed each user’s ratings of the items in your category to help you predict whether someone would like one item based on their feelings about other, similar items.

Now, reflect on your experience building a recommendation system.

Open your machine learning journal from Google Drive.

Or, create a new, blank document and name it “Machine Learning Journal.”

Add a heading that says “Build a Recommendation System Reflection.”

Do you think your model would work well for recommending new items to users?

If yes, why do you think so?

If no, what questions or concerns do you have about the process?

Did you get enough data from your classmates?

Do you think your category might have been too broad?

Did the items you chose fit the category well?

Would a machine have trouble finding patterns in your data?

Reflect on these questions and add the answers to your journal.

You do not need to type the questions in your journal.

They are just on the screen for you to reference as you write your responses.

Next, move on to the next video to wrap up this lesson.

Now, it’s your turn: Open your machine learning journal from Google Drive, or create a new, blank document.

Add a heading.

Reflect on what you learned.

And write your thoughts.

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