Great job finishing the course on data preparation! Now, let's see how we can use that data in Machine Learning.
You probably interact with ML every day—when Netflix suggests shows, your phone recognizes faces in photos, or your email filters out spam.
Engagement Message
Can you think of another example where you use or notice machine learning in your daily life?
At its core, machine learning is teaching computers to find patterns in data and make predictions. While it's called "learning," it's really about using data to make educated guesses—machines don't truly understand or reason like humans or advanced AI models, but instead follow patterns found in the data.
Engagement Message
Can you think of a situation where making a good guess from patterns is more useful than knowing the exact answer?
There are three main ways machines learn, just like how humans learn differently in various situations.
Think about learning to drive: you might learn from an instructor (guided), explore on your own (discovery), or improve through trial and error (feedback).
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Which learning style sounds most familiar to you?
Supervised Learning is like learning with a teacher. You show the computer many examples with correct answers, and it learns to predict answers for new examples.
Email spam detection works this way—feed it thousands of emails labeled "spam" or "not spam."
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What other "teacher-guided" examples can you imagine?
Unsupervised Learning is like discovering patterns without a teacher. The computer finds hidden structures in data without being told what to look for.
Netflix grouping customers by viewing habits is unsupervised—no one labeled customer types beforehand.
Engagement Message
Can you think of another situation where a system clusters people or items without any labels?
