Open Source For You — December 2017

80 | DECEMBER 2017 | OPEN SOURCE FOR YOU | http://www.OpenSourceForU.com

Developers Insight

Unsupervised learning: The objective of unsupervised

learning algorithms is to represent the hidden structure of

the data set in order to learn more about the data. Here, we

only have input data with no corresponding output variables.

Unsupervised learning algorithms develop the descriptive

models, which approach the problems irrespective of the

knowledge of the results. So it is left to the system to find

out the pattern in the available inputs, in order to discover

and predict the output. From many possible hypotheses, the

optimal one is used to find the output.

Sorting apples and mangoes from a basket full of fruits

can be done using unsupervised learning too. But this time the

machine is not aware about the differentiating features of the

fruits such as colour, shape, size, etc. We need to find similar

features of the fruits and sort them accordingly.

Some of the algorithms we can use here are the K-means

clustering algorithm and hierarchical clustering.

Reinforcement learning: In this learning method, ideas

and experiences supplement each other and are also linked

with each other. Here, the machine trains itself based on the

experiences it has had and applies that knowledge to solving

problems. This saves a lot of time, as very little human

interaction is required in this type of learning. It is also called

the trial-error or association analysis technique, whereby the

machine learns from its past experiences and applies its best

knowledge to make decisions. For example, a doctor with

many years of experience links a patient’s symptoms to the

illness based on that experience. So whenever a new patient

comes, he uses his experience to diagnose the illness of the

patient.

Some of the algorithms we can use here are the Apriori

algorithm and the Markov decision process.

Machine learning applications

Machine learning has ample applications in practically every

domain. Some major domains in which it plays a vital role are

shown in Figure 7.

Banking and financial services: Machine learning plays

an important role in identifying customers for credit card

offers. It also evaluates the risks involved with those offers.

And it can even predict which customers are most likely to be

defaulters in repaying loans or credit card bills.

Healthcare: Machine learning is used to diagnose fatal

illnesses from the symptoms of patients, by comparing them

with the history of patients with a similar medical history.

Retail: Machine learning helps to spot the products that

sell. It can differentiate between the fast selling products

and the rest. That analysis helps retailers to increase or

decrease the stocks of their products. It can also be used to

recognise which product combinations can work wonders.

Amazon, Flipkart and Walmart all use machine learning

to generate more business.

Publishing and social media: Some publishing firms

use machine learning to address the queries and retrieve

documents for their users based on their requirements and

preferences. Machine learning is also used to narrow down

the search results and news feeds. Google and Facebook are

the best examples of companies that use machine learning.

Facebook also uses machine learning to suggest friends.

Games: Machine learning helps to formulate strategies for

a game that requires the internal decision tree style of thinking

and effective situational awareness. For example, we can build

intelligence bots that learn as they play computer games.

Face detection/recognition: The most common

example of face detection is this feature being widely

available in smartphone cameras. Facial recognition has

even evolved to the extent that the camera can figure out

when to click – for instance, only when there is a smile on

the face being photographed. Face recognition is used in

Facebook to automatically tag people in photos. It’s machine

learning that has taught systems to detect a particular

individual from a group photo.

Genetics: Machine learning helps to identify the genes

associated with any particular disease.

Figure 6: Unsupervised learning model (Image credit: Google)

Figure 7: Machine learning applications

Training

Text

Documents,

Images,

Sounds...

Machine

Learning

Algorithm

New

Text

Document,

Image,

Sound...

features

vector Model

Likelihood

or

Cluster Id

or

Better

representation

features

vectors