Apache Spark Machine Learning | Apache Spark Tutorial For Beginners | Simplilearn Video Lecture | Taming the Big Data with HAdoop and MapReduce - Software Development

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let's first understand what machine
learning is it's a subfield of
artificial intelligence that has
empowered various smart applications it
deals with the construction and study of
systems that can learn from data for
instance consider the photo album
feature of Facebook this feature has the
capability to learn from the data and
hence recognize the faces that can be
tagged in a picture
similarly the Siri application of Apple
also has the capability to learn from
the data and hence analyze the human
voice meaning to perform the desired
action or provide the desired answers
LinkedIn and Google driverless car also
work on the same concept therefore the
objective of machine learning is to let
a computer predict something an obvious
scenario is to predict an event of the
future apart from this it also covers to
predict unknown things or events this
means that something that has not been
programmed or inputted in it in other
words computers act without being
explicitly programmed it can be seen as
building blocks to make computers behave
more intelligently in the words of
Arthur Samuel in 1959 machine learning
is a field of study that gives computers
the ability to learn without being
explicitly programmed later in 1997 Tom
Mitchell gave another definition that
proved more useful for engineering
purposes a computer program is said to
learn from experience II with respect to
some tasks T and some performance
measure P if its performance on T as
measured by P improves with experience
E
sometimes machine learning is related
with data mining however it is more
focused towards exploratory data
analysis in addition pattern recognition
and machine learning are also related
and can be seen as two facets of the
same area a few examples of the machine
learning applications are listed on the
screen
the scalable machine learning library of
spark is ML library
it contains general learning utilities
and algorithms which include regression
collaborative filtering classification
clustering dimensionality reduction and
underlying optimization primitives there
are two types of API available
the primary API is original spark ml
library API and a higher-level API to
construct machine learning workflows is
pipelines spark ml
let's first talk about data frames
machine learning is applicable to
various data types which include text
images structured data and vectors to
support these data types under a unified
data set concept SPARC ml includes the
SPARC SQL data frame these data frames
provide support to various basic types
structured types and ml vector types you
can create a data frame from a regular
RTD either implicitly or explicitly hey
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