Machine Learning is the art of getting computers to grasp and act as humans do, and upgrade their learning over time in a self-governed fashion, by nourishing them with data and information in the form of observations and real-world relationships.
The machine adapts to the users based on the data.
Example: Online Shopping
You need to open any shopping site and search for any product. 2nd time when you open the same shopping site and you will see on the Home page, you will be seen some products which are related to your previous search.
Whatever you doing the 1st time, observing your search, observing your choices, observing the things you are clicking, Machine will learn something and Machine will get a pattern, and according to that pattern, 2nd time when you open the application, Machine automatically will send you the suggestion and that is the concept of the Machin Learning.
Artificial Intelligence (AI) and Machine Learning are influenced by human behavior.
How Machine Learning is different from Traditional Programming?
Traditional Programming is a manual process. In traditional programming, a developer designs logic or algorithms to solve a problem. The program relates this logic to the input and computes the output.
In Machine Learning, a model is created from the data, and that model is the logic. For example, if you feed in customer statistics, transactions as input, and the ascertained output if they churned or not in the past, the algorithm will codify the program which would know how to conclude if someone would churn or not. That program is called a model.
This is the primary difference between traditional programming and machine learning. Without anyone programming logic, in Traditional programming one has to manually draft the rules, while in Machine Learning the algorithms automatically define the rules from the data.
Summary: Traditional programming is centrally different from Machine Learning. In Traditional programming, the program uses the data in a logical way to complete a task. Machine learning uses data to reach a logic that can further be used to declare patterns.
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