In my last post, I gave you a teaser to what “Machine Learning” is. Taking things up a notch, let us dive into the deep seas of Machine Learning(ML).

Deep ML seas(don’t confuse with Deep Learning) have *variations, curves, biasness, data and its diversity*. There are different types of ML algorithms to tackle these stuffs.

Broadly, these ML algorithms can be divided into 3 categories:

**Supervised Learning –** *To tackle the data which have a known answer.*

**Unsupervised Learning –** *To tackle the data which have no known answer.*

**Reinforcement Learning –** *To tackle the data based on final outcome.*

## Supervised Learning

*The game of the labeled data resides in this colony of ML..*

The word labeled data signifies that for a given values of a set of input variables(x) we will have a given value of output variable(y; labels). The algorithms belonging to this category generally gives you a mapping

y = f(x)

which gives the best possible relation between input and output variables.

Think of the algorithm being a student supervised by a teacher. Teacher, being aware of the correct answers, corrects his student(algorithm) on each iteration till the student(algorithm) gives an acceptable level of performance.

Going further down the lane, we can divide supervised learning problems in two sub-categories:

**Classification:** A problem where output variable is a category or a class, such as “Cancer positive” or “Cancer negative”.

**Regression:** A problem where output variable is a real value, such as “rupees” or “weight”.

We’ll further look into these deep corners of supervised learning in upcoming posts.

## Unsupervised Learning

*Have cluttered, unlabeled data – we’ve you covered..!!*

Unsupervised Learning problems are where you have input data but no corresponding output.

The algorithms covering this domain have a goal to model the underlying data into meaningful structures in order to learn more about the data gathered.

Consider these algorithms as those innovative students who don’t have a supervisor(teacher) to guide them. They are left on their own with the data to fiddle with it and give it a structure as they like. *“…Mischievous!!”*

Unsupervised Learning problems can further be divided into two sub-categories:

**Clustering:** A problem to find the inherent groupings in the data, such as grouping customers by purchasing behavior.

**Association:** A problem to discover association between large portions of data, such as people that buy X tend to buy Y.

We’ll cover these sub-categories in the future posts, tackling one at a time.

## Reinforcement Learning

*Greed for reward is the key..!!*

In Reinforcement Learning problems, a software agent adapts in an environment so as to maximise its rewards.

To understand it, consider teaching a dog a new trick – there is no way to tell a dog “what to do!”, but you can reward or punish it depending on it being right or wrong.

These algorithms can, in a similar manner, train computers to perform complex tasks, such as playing chess. In Reinforcement Learning problems, if the modelling of problem is well handled, some algorithms can even converge to global optimum (that is, to the ideal behavior that maximises reward).

While we are talking of Greed..let me give you a greedy motivation to give your best in this field and keep going.

A breakthrough in machine learning would be worth ten Microsoft’s

-Bill Gates