Mission: River Beautification

Suppose you are preparing for a driver's license test. Instead of learning all traffic rules, you learn how to drive the car. In the beginning, it is hard, but as you find some patterns while observing the way you drive, you learn to drive. Exactly in the same way, ML works; it does not memorize the data but extracts patterns from the data and then creates the best-fit line for the dataset.

Machine Learning is all about finding patterns and relationships in the data to use it in future predictions. It enables computers to learn from data and make predictions without explicit programming for specific tasks.
Machine Learning can do everything; using machine learning, one can create a self-driving car or a math equation solver.

To know it simply, here is an example:

Suppose you are preparing for a driver's license test. Instead of learning all traffic rules, you learn how to drive the car. In the beginning, it is hard, but as you find some patterns while observing the way you drive, you learn to drive. Exactly in the same way, ML works; it does not memorize the data but extracts patterns from the data and then creates the best-fit line for the dataset.

Spam Email Detection: Using ML, we classify emails as spam, as the ML learns the pattern in which spammers send the email what words they use and how they use those words. So using ML, we can classify emails into spam.

In the same way, we can predict multiple things like credit card fraud, cancer, etc.

Suppose you are preparing for a driver's license test. Instead of learning all traffic rules, you learn how to drive the car. In the beginning, it is hard, but as you find some patterns while observing the way you drive, you learn to drive. Exactly in the same way, ML works; it does not memorize the data but extracts patterns from the data and then creates the best-fit line for the dataset.