What is ML?

One of the main differences between humans and computers is that humans learn from past experiences and computers need to be told what to do. Computers need to be programmed to follow instructions.

Well, the question is how can we get computers to learn from experiences? There you go. The answer is Machine learning.

In context of computers, the we call experience as "Data"

The Main Idea Behind ML

The main idea in ML is that the there are generic algorithms that can tell something interesting about a data set without humans having to write any custom code specifically

ML is a scalable approach

If we want to recognize objects in a picture, in the old days, programmers would have to write code for every object they wanted to recognize. eg: a cat or a dog. This is not a scalable approach.

With ML, the algorithm can be trained with the training data that tells the algorithm what a cat or a dog is in a bunch of given pictures. The algorithm is corrected every time is wrongly identifies a cat in a given picture. if shown a series of new pictures, it begins to identify cat photos in the new set.

Machine learning is an umbrella term covering these generic algorithms aimed at teaching computers to learn form data