Open Source For You — December 2017

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

Developers Insight

M

achine learning is a set of methods by which

computers make decisions autonomously. Using

certain techniques, computers make decisions by

considering or detecting patterns in past records and then

predicting future occurrences. Different types of predictions

are possible, such as about weather conditions and house

prices. Apart from predictions, machines have learnt how

to recognise faces in photographs, and even filter out email

spam. Google, Yahoo, etc, use machine learning to detect

spam emails. Machine learning is widely implemented across

all types of industries. If programming is used to achieve

automation, then we can say that machine learning is used to

automate the process of automation.

In traditional programming, we use data and programs

on computers to produce the output, whereas in machine

learning, data and output is run on the computer to produce a

program. We can compare machine learning with farming or

gardening, where seeds --> algorithms, nutrients --> data, and

the gardener and plants --> programs.

We can say machine learning enables computers to learn

to perform tasks even though they have not been explicitly

programmed to do so. Machine learning systems crawl

through the data to find the patterns and when found, adjust

the program’s actions accordingly. With the help of pattern

recognition and computational learning theory, one can study

and develop algorithms (which can be built by learning

from the sets of available data), on the basis of which the

computer takes decisions. These algorithms are driven by

Machine learning is a fascinating study. If you are a beginner or simply curious

about machine learning, this article covers the basics for you.

building a model from sample records. These models are

used in developing decision trees, through which the system

takes all the decisions. Machine learning programs are also

structured in such a way that when exposed to new data,

they learn and improve over time.

Implementing machine learning

Before we understand how machine learning is implemented

in real life, let’s look at how machines are taught. The

process of teaching machines is divided into three steps.

1 Data input: Text files, spreadsheets or SQL databases

are fed as input to machines. This is called the training

data for a machine.

2 Data abstraction: Data is structured using algorithms

to represent it in simpler and more logical formats.

Elementary learning is performed in this phase.

3. Generalisation: An abstract of the data is used as
   input to develop the insights. Practical application
   happens at this stage.
   The success of the machine depends on two things:
   ƒ How well the generalisation of abstraction data happens.
   ƒ The accuracy of machines when translating their learning
   into practical usage for predicting the future set of actions.
   In this process, every stage helps to construct a better
   version of the machine.
   Now let’s look at how we utilise the machine in real life.
   Before letting a machine perform any unsupervised task, the
   five steps listed below need to be followed.

Insights into Machine Learning