Types of Machine Learning Algorithm

After having knowledge about history and definition of machine learning let's move forward to see various types and terminologies regarding to this Machine Learning.

Machine Learning is divided into 4 type:

1.Supervised Learning: In this type of learning input and output of the datasets are given and these are passed to the learning algorithm

which predict some model.In this model new input is given and then output is produced.

2.Unsupervised Learning:In this type of learning we are given only input and these input are cluster or summarize to produce the output.

3.Semi supervised Learning:It is a class of supervised learning tasks and techniques that also make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.

4. Reinforcemnt Learning:  It is concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.Simple reward feedback is required for the agent to learn its behavior. This is known as the reinforcement signal.

Reinforcement Learning allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance. 

Given below is the video in which I have explain in detail about the types of learning and various terminologies used in learning algorithm:

Hope you have enjoyed reading this article.From next article I will be discussing about Inductive Learning.Till then enjoy learning!!!