What is Machine Learning?

Machine learning is born when Coding language intersects with Statistics and Mathematics. In other words, Machine learning is everything that you can think of without Domain expertise coming into the picture. 

The Definition: So what is Machine learning in concise? Machine learning is set of Computer Programs that can access data and use it to automatically learn and improve from experience.

Machine learning is not restricted to a mere certain application for a particular domain.

We shall illustrate Machine learning by use of the following example.

The graph above portrays Bella’s food preference. Bella likes her food to be real spicy and hot. Lets imagine, Bella is served a food that belongs to a Moroccan cuisine. The food is spicy and is served hot, the question is, would Bella like the food? For sure, yes, why? because, the food group lies in the list of Bella’s classified preferences- Spicy and Hot. Just by understanding Bella’s past choices, we would clearly be able to ascertain what are Bella’s preferences and thus classify this unknown food type very easily.

Now let us imagine, Bella is served another variety food which is between mildly spicy and more spicy and that the food is served at a temperature that is between mildly hot and real hot. Now what are chances that Bella would love the food?

Now let us
imagine, Bella is served another variety food which is between mildly spicy and
more spicy and that the food is served at a temperature that is between mildly
hot and real hot. Now what are chances that Bella would love the food?

Now this gets a bit complicated, why? because the data given about the food lies in between the groups that we have information about. This is where Machine learning comes in. if we were to draw a line that would include part of the two groups that we had already mentioned so as to contain the new food group, then we would clearly see that there are 2 food choices in the high spicy and hot group versus, 1 food choice in the mildly spicy and mildly warm region, this would mean, it is 2 against 1.

In other
words, the likelihood of Bella liking the food is large, given the vote
differences that we observe. This is how a basic Machine learning Algorithm
functions, and we have just applied the concept of K-Nearest Neighbors
Algorithm.

The machine,
learns the data, builds a model, and when a new data enters the scene, it
predicts. In brevity, the efficiency of Machine learning depends on large voluminous
data, for higher the volume, better the model, and better the model, higher the
efficiency.

The Complete Machine Learning Process