Machine Learning Basics
What is Machine learning?
Imagine a 2 year old named Debbie. Debbie doesn’t know anything at first. You want to teach Debbie how to recognize different birds. What do you do?
To teach Debbie to recognize different birds, you start showing her lots of pictures of peacock and crow. You tell Debbie “This is a peacock” and “This is a crow” as you show her each picture. Debbie starts to notice the difference between the two: peacocks have colorful feather tails, while crows are solid black. Peacocks have a crest on their heads, and crows have beaks more curved than peacocks.
After seeing hundreds of pictures of peacocks and crows and learning their features, Debbie can now recognize new peacock and crow pictures she hasn’t seen before! If you show her a new picture and ask “Is this a peacock or crow?” Debbie can correctly tell you the answer.
This is machine learning!
Debbie is the machine. By showing her examples (pictures of peacock or crow labels), Debbie can learn to recognize patterns and make predictions about new unseen pictures. The more examples Debbie learns from, the better she gets.
And there you have it — a simple analogy to understand the essence of machine learning! Little Debbie was able to learn to recognize birds by being shown many labeled examples.
Just like Debbie, machine learning algorithms can be trained to identify patterns, classify data, and make predictions by learning from large datasets. The possibilities are endless — machine learning is revolutionizing everything from self-driving cars to personalized product recommendations.
Stay tuned…in my next post, we’ll dive deeper into the different types of machine learning algorithms and how they work their magic!
