Ok, so what is Machine Learning? Well, I hope I am able to explain it to you in a way that's easy to understand.

Machine Learning is a set of methods which enables the computer to take decisions or draw out conclusions without us having to guide it.

Safe to say, a feature that imitates and adapts human like behaviour.

Now, granny, you would ask me how does a machine imitate us. A very valid point to ask. So just like how we humans would see, observe our surroundings and try to come up with the best possible solutions to the situations, or probably observing what others did in similar situations, or even to just learn from various past experiences, machines also execute it in the same way.

There are two types of ML Techniques:

i) Supervised Learning

ii) Unsupervised Learning

Supervised Learning:

Finds patterns (and develops predictive models) using both, input and output data.
All Supervised Learning techniques are a form of either Classification or Regression.

1.Classification:

Classification is used for predicting discrete responses.
For example: Will it rain tomorrow or not?

2.Regression:

Regression is used for predicting continuous responses.
For example: Weather forecast, etc.

Unsupervised Learning:

Finds patterns based only on input data. This is useful when you’re not quite sure what to look for(output).

Some common applications of Machine Learning that you can relate to:

1. Your personal Assistant Siri or Google uses ML.
2. Weather predictions for the next week comes using ML.
3. Win Predictor in a sports tournament uses ML.
4. Medical Diagnosis dominantly uses ML.

So granny, that's Machine Learning for you.