Machine Learning has the three different flavours depending on the algorithm and their objectives they serve . One can divide machine learning algorithms into three main groups based on the purpose :
01) Supervised Learning
02) Unsupervised Learning
03) Re-inforcement Learning
Now in this article we will learn more on each of the learning techniques in greater detail .
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01) Supervised Learning
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* Supervised Learning occurs when an algorithm learns from a given form of example data and associated target responses that consist of numeric values or string labels such as classes or tags , which can help in later prediction of correct responses when one is encountered with newer examples
* The supervised learning approach is similar to human learning under the guidance and mentorship of a teacher . This guided teaching and learning of a student under the aegis of a teacher is the basis for Supervised Learning
* In this process , a teacher provides good examples for the student to memorize and understand and then the student derives general rules from the specific examples
* One can distinguish between regression problems whose target is a numeric value and along with that one can make use of such regression problems whose target is a qualitative variable which is an indicator of a class or a tag as in the case of a selection criteria
* More on Supervised Learning Algorithms with examples would be discussed in later articles .
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02) Unsupervisd Learning
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* Unsupervised Learning occurs when an algorithm learns from plain examples without any associated response in the target variable , leaving it to the algorithm to determine the data patterns on their own
* This type of algorithm tends to restructure the data into something else , such as new features that may represent a class or a new series of uncorrelated values
* What is Unsupervised Learning ? It is a type of learning which tends to restructure the data into some new set of features which may represent a new class or a series of uncorrelated values within a data set
* Unsupervised Learning algorithms are quite useful in providing humans with insights into the meaning of the data as there are patterns which need to be found out
* Unsupervised Learning is quite useful in providing humans with insights into the meaning of the data and new useful inputs to supervised machine learning algorithms
* As a new kind of learning , Unsupervised Learning resembles the methods that humans use to figure out that certain objects or events are of the same class or characteristic or not , by observing the degree of similarity of the given objects
* Some of the recommendation systems that one may have come across over several retail websites or applications are in the form of marketing automation which are based on the type of learning
* The marketing automation algorithm derives its suggestions from what one has done in the past
* The recommendations are based on an estimation of what group of customers that one resembles the most and then inferring one's likely preferences based on that group
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02) Reinforcement Learning
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* Reinforcement Learning occurs when one would present the algorithm with examples that lack any form of labels as in the case of unsupervised learning .
* However , one can provide an example with some positive and negative feedback according to the solution of the algorithm proposed
* Reinforcement Learning is connected to the applications for which the algorithm must make decisions ( so the product is mostly prescriptive and not just descriptive as in the case of unsupervised learning ) and on top of that the decisions bear some consequences .
* In the human world , Reinforcement learning is mostly a process of learning by the application of trial and error method to the process of learning
* In this type of learning , initial errors and aftermath errors help a reader to learn because this type of learning is associated with a penalty and reward system which gets added each time whenever the following factors like cost , loss of time , regret , pain and so on get associated with the results that come in the form of output for any particular model upon which the set of reinforcement learning algorithms are applied
* One of the most interesting examples on reinforcement learning occurs when computers learn to play video games by themselves and then scaling up the ladders of various levels within the game on their own just by learning on their own the mechanism and the procedure to get through each of the level .
* The application lets the algorithm know the outcome of what sort of action would result in what type of result .
* One can come across a typical examplle of the implementation of a Reinforcement Learning program developed by Google's Deep Mind porgram which plays old Atari's videogames in a solo mode at https://www.youtube.com/watch?v=VieYniJORnk
* From the video , one can notice that the program is initially clumsy and unskilled but it steadily improves with better continuous training until the program becomes a champion at performance of the task
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