My blog on types of Machine Learning Algorithm.
By: Dheeraj Chavan
Tags: Datascientist, Bigdata, Dataanalytics, Machinelearning

My blog on types of Machine Learning Algorithm.



Hi friends today I am going to introduce some basic information regarding Machine Learning which the data scientist students should know.



Coming to are topic first we will know actually what is Machine Learning.

Machine learning is an application that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

In Machine Learning basically there are three different types of learning.

1) Supervised Learning.

2) Unsupervised Learning.

3) Reinforcement Lqearning.

1] Supervised Learning:

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.

In this learning there are two main types,.

* Classification:

It involves grouping the data into classes. If you are thinking of extending credit to a person, you can use classification to determine whether or not a person would be a loan defaulter. When the supervised learning algorithm labels input data into two distinct classes, it is called binary classification. Multiple classifications means categorizing data into more than two classes.

* Regression:

In regression, a single output value is produced using training data. This value is a probabilistic interpretation, which is ascertained after considering the strength of correlation among the input variables. For example, regression can help predict the price of a house based on its locality, size, etc.

2] Unsupervised Learning:

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.

The most common types under this learning is:

* Clustering:

It is a process of segregating a huge number of items into a small groups sharing similar characteristics.

For example,

If we have the list of all persons in Asia, We can group them based on their nationalities like

Group 1: People belonging to India

Group 2: People belonging to China

Group 3: People belonging to Nepal etc...

* Association:

Is a type of unsupervised learning technique that checks for the dependency of one data item on another data item and maps accordingly so that it can be more profitable. It tries to find some interesting relations or associations among the variables of dataset.

Example :Groups of shopper based on their browsing and purchasing histories.

3] Reinforcement Learning:

Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience.

In this learning there are four types:

* Positive.

* Negative.

* Punishment.

*extinction.

I will be explaining more briefly about machine learning topics in my upcoming blogs.