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Do you know?
Machine
learning (ML) is the study of computer algorithms that improve
automatically through experience. It is seen as a subset of
artificial intelligence. Machine learning algorithms build a model
based on sample data, known as "training data", in order to make
predictions or decisions without being explicitly programmed to do
so. Machine learning algorithms are used in a wide variety of
applications, such as email filtering and computer vision, where
it is difficult or unfeasible to develop conventional algorithms
to perform the needed tasks.
A subset of machine learning is closely related to computational
statistics, which focuses on making predictions using computers;
but not all machine learning is statistical learning. The study of
mathematical optimization delivers methods, theory and application
domains to the field of machine learning. Data mining is a related
field of study, focusing on exploratory data analysis through
unsupervised learning. In its application across business
problems, machine learning is also referred to as predictive
analytics.
Data
science is an inter-disciplinary field that uses scientific
methods, processes, algorithms and systems to extract knowledge
and insights from many structural and unstructured data. Data
science is related to data mining, machine learning and big data.
Data science is a "concept to unify statistics, data analysis and
their related methods" in order to "understand and analyze actual
phenomena" with data. It uses techniques and theories drawn from
many fields within the context of mathematics, statistics,
computer science, domain knowledge and information science. Turing
award winner Jim Gray imagined data science as a "fourth paradigm"
of science (empirical, theoretical, computational and now
data-driven) and asserted that "everything about science is
changing because of the impact of information technology" and the
data deluge
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