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Renuncia de responsabilidad: El siguiente video se tradujo mediante un software de traducción y se dobló con una voz artificial para tu comodidad, todo ello de forma automática. Si deseas obtener más detalles, consulta la renuncia de responsabilidad completa.
In this lesson, you: Worked with a spreadsheet that contained dataabout a pretend person’s driving habits,Used a pivot table to count the number of times the user visited each place,Created a map of the locations they visited,And made inferences about the pretend person based on the data.
You also learned that, while data can provide lots of useful information, inferences canbe wrong.
For example, maybe you thought someone who frequents a dog park has a dog.
But they may just like to exercise, and it’s the closest park to their home.
Or perhaps they go there to meet up with a friend who is a dog owner.
Take a few minutes to compare the inferences you made to those of your classmates who chosethe same starter project.
Did you come to the same conclusions?
How did your inferences differ?
Now, reflect on this lesson.
To record your thoughts, open a new Google document.
Name it “Machine Learning Journal.”
Then, add a heading called “Data Lesson Reflection.”
Next, consider the following questions: Who would you want to share your own drivingdata with?
Your social media followers?
Why might you want to share your driving data?
Why might you not want to share your driving data?
What inferences could people make about you from the places you visit?
What are the pros and cons of using data to help machines learn?
Then, list at least two people, groups, or businesses that you would share your drivingdata with and why.
Next, list at least two people, groups, or businesses that you would notshare this data with and why.
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.
Even though you might not want everyone to have access to your information, there aremany positive reasons for sharing data and using machine learning.
It can lead to new and exciting technologies, create a safer environment, and help peoplebe more connected in the world.
But it is important to understand what data you share, who you share it with, and howit is being used.
Keep learning more about how people, groups, and businesses use the data you create tomake inferences about you.