In these lessons, you explored machine learning in four ways.
You examined simulated data from a pretend person driving their car.
You mapped the user’s locations and made inferences based on the data presented.
You created a survey in Google Forms and collected ratings for items in a category.
You created a model to help you predict how much someone would like a different product.
Your recommendation system simulated, in a very basic way, how computers use data to
You explored how computers learn based on the data they receive.
You used a machine learning app to teach a computer about a set of images and then saw
how accurately the computer could identify other images in the same category.
You considered the benefits and drawbacks of using machine learning to make important
And you collected your thoughts about machine learning in a journal.
It’s almost impossible to comprehend how much data is available in the world today.
In fact, the amount of information humans produce “every single day” is said to
be about two point five “quintillion” bytes of data -- and that pace is only accelerating.
Turning to machine learning for help is a smart step toward making sense of all of this
However, training computers is extremely challenging -- and has both benefits and drawbacks.
Machine learning has the potential to make the world safer, more fun, and more efficient.
For example, in order to help ships avoid harming whales at sea, machine learning is
enabling users to make sense of ocean noises!
Scientists taught machines to recognize whale sounds, and the models they created as a result
enable ship workers to identify and avoid whales.
Still, machine learning also has some drawbacks.
As you discovered, if a computer trains on data that shows favoritism, self-interest,
bias, or deception, this can lead to serious issues in the real world.
It’s important to keep these things in mind because machine learning is already a big
part of your day-to-day activities -- and will likely grow and influence many other
aspects of your life.
Keep writing in your machine learning journal if you have new questions, ideas, or concerns
about these important topics!