Working with Data in Machine Learning
* Machine Learning is one of the most appealing subjects because
it allows machines to learn from real world examples such as sales records ,
signals from sensors and textual datastreaming from internet and then determine
what such data would imply with the help of that subject
* The most common outputs that can commence from machine
learning algorithms is prediction of the future , prescriptions and
prescriptive knowledge for design and build up of applications etc
* Some of the common outputs that can come from machine learning
algorithms is the following : prediction of the future , prescription to act on
some given knowledge or information , creation of new knowledge in terms of
examples categorised by groups
* Some of the applications which are already in place and have
become a reality thanks to leveraging the use of such knowledge are the
following things :
01) Diagnosing hard to find diseases
02) Discovering criminal behaviour and detecting criminals in action
03) Recommending the right product to the right person
04) Filtering and classifying data from internet at an big scale
05) Driving a car autonomously etc
* The mathematical and statistical basis of machine learning
makes outputting such useful results possible
* One can use Math and Statistics over such accumulated data
which could enable algorithms to understand anything with a numerical basis
* In order to begin the process of working with Data , one
should represent the solution to the problem in the form of a number .
* For example , if one wants to diagnose a disease using a
machine learning algorithm , one can make the response to a particular learning
problem a 1 or 0 (binary response) which would inform about the illness of the
person . A value of 1 would indicate that the person is ill , with a value of 1
stating that the person is ill or not .
Alternatively , one can use a number between the values 0 and 1 to convey an
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