What is Machine Learning and why is it important or useful?
Machine Learning is the ability to learn without being explicitly programmed.
Well, what does that mean?
In essence, computers or CPUs are pretty dumb if we don't program them. Without AI, a computer usually works in a simple input to output paradigm. We input a command either through programming or UI ... for example opening Word, and then the computer responds with opening the program ... as an output to our action. Another example is your calculator: we input 5 x 2 and the calculator outputs 10 as a response.
So this simple input-output process is how computer used to work before AI started to permeate every piece of software.
In a machine learning paradigm, there is another factor added to this input to output equation ... the learning part. We have an input, learning model and then the output. In this paradigm, the machine learns from your inputs and makes better output over time.
For example, a simple Google search: If you were to type and make the mistake to type into Google Search this query: "What is the biggest dessert in the world". When you actually meant "desert". In a simple input to output paradigm Google may show you the biggest cookie or cake in the world. But because Google is build with AI assistance, it will have inferred that when someone is asking for the "biggest dessert in the world" they are probably are looking for a "desert" and not a food.
And this is where machine learning comes into play. Over years of gathering data or training the Machine Learning model, Google has learned that in most cases when someone is searching for the "biggest dessert in the world", they truly mean "desert" and that is the important item of machine learning... it needs to be trained for its model to be efficient, accurate or in other words intelligent.
A machine learning paradigm needs to be fed with hundreds, thousands of more data set to work.
There are a lot of algorithms for machine learning concept: supervised machine learning algorithms or unsupervised machine learning algorithms or semi-supervised machine learning algorithms or reinforcement machine learning algorithms ... but now it's your turn to document yourself.