Fundamental concepts:
·
Before start with the data science let us discuss
about the fundamental formation of data science. Following Figure-1 illustrate the structure of all fundamental fields related with the data science.
·
Data science basically consists of following fields:
i. Artificial
Intelligence (AI)
ii. Machine
Learning (ML)
iii. Deep
Learning (DL)
i.
Artificial
Intelligent (AI):
·
Artificial Intelligence is the combination of science
and engineering for making intelligent machine.
·
In sense of these AI is a technique for making machines
to think and behave similar like human being.
·
For example car automation system and android app like
Google map are the examples of AI.
ii.
Machine
Learning (ML):
·
Machine Learning (ML) is a subset of Artificial Intelligence
(AI) which uses statistical tools to explore and analyze the data.
·
Machine Learning (ML) is mainly divided into three
types:
o
Supervised
Learning: In this type it uses labelled data and algorithms like
Regression and classification etc.
o
Unsupervised
Learning: This type uses unlabelled (data defined without categories or group)
data and algorithms like clustering KNN, K-Means etc.
o
Semi Supervised
Learning: The combination of some labelled and some unlabelled data which
is called as Semi Supervised Learning. For example Speech Analysis, Internet
Content Classification etc. Algorithms used like generative models and graph
based models.
iii.
Deep
Learning (DL):
·
Deep Learning (DL) is a subset of Machine Learning (ML)
which creates architecture like Multi Neural Network to mimic like a human brain.
·
Deep Learning (DL) is mainly divided into three types:
o
ANN (Artificial Neural Network): Data set in the form
of numbers.
o
CNN (Convolutional Neural Network): Data set in the
form of images.
o
RNN (Recurrent Neural Network): Data set in the form of
time series data.
Data Science:
Data Science apply all the technique of AI, ML and DL using methods like Statistics,
Linear algebra, Probability, Differential calculus etc.
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