Machine Learning Introduction | Machine Learning Tutorial | Simplilearn Video Lecture | Taming the Big Data with HAdoop and MapReduce - Software Development

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in this topic you will learn about
machine learning it's applications and
standard machine learning clustering
algorithms like k-means let's first
understand what machine learning is it
is 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 machine
learning can be used in medical
diagnosis to answer a question such as
is this cancer it can learn from data
and help diagnose a patient as a
sufferer or not
another example is fraud detection where
machine learning can learn from data and
provide an answer to a question such as
is this credit card transaction
fraudulent therefore the objective of
machine learning is to let a computer
predict something an obvious scenario is
to predict an event in future apart from
this it can also predict unknown things
or events this means that something that
has not been programmed or inputted into
it in other words computers act without
being explicitly programmed machine
learning can be seen as building blocks
to make computers behave more
intelligently in 1959 Arthur Samuel
defined machine learning as 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
machine learning computer program is
said to learn from experience II with
respect to some class of tasks T and
performance measure P if its performance
at tasks in T as measured by P improves
with experience II as data plays a big
part in machine learning it's important
to understand
and use the right terminology when
talking about data this will help you to
understand machine learning algorithms
in general let's begin with vector
feature it is an n-dimensional vector of
numerical features that represent some
object it is a typical setting which is
provided by objects or data points
collection each item in this collection
is described by a number of features
such as categorical and continuous
features samples are the items to
process examples include a row in a
database a picture or a document feature
space refers to the collection of
features that are used to characterize
your data in other words feature space
refers to the n dimensions where your
variables live if a feature vector is a
vector length L each data point can be
considered being mapped to a d
dimensional vector space called the
feature space labeled data is the data
with known classification results once a
labeled data set is obtained you can
apply machine learning models to the
data so that the new unlabeled data can
be presented to the model a likely label
can be guessed or predicted for that
piece of unlabeled data here is an
example of features of two apples one is
red and the other is green in machine
learning an object is used in this
example the object is the Apple the
features of the object which is Apple
include color type and shape in the
first instance the color is red the type
is fruit and the shape is round in the
second instance there is a change in the
featured description in the color of the
Apple which is now green as machine
learning is related to data mining it is
a way to fine-tune a system with tunable
parameters it can also identify patterns
that humans tend to overlook or are
unable to find quickly among large
amounts of data as machine learning is
transforming a wide variety of
industries
helping companies make new discoveries
and identify and remediate issues faster
here are some interesting real-world
applications of machine learning speech
recognition machine learning has
improved speech recognition systems
machine learning uses automatic speech
recognition or ASR as a large-scale
realistic application to rigorously test
the effectiveness of a given technique
effective web search machine learning
techniques such as naivebayes extract
the categories or a broad range of
problems from the user entered query to
enhance the results quality this is
based on query logs to train the model
recommendation systems according to
Wikipedia recommendation systems are a
subclass of information filtering system
that seek to predict the rating or
preference that a user would give to an
item recommendation systems have been
using machine learning algorithms to
provide users with product or service
recommendations here are some more
applications of machine learning
computer vision computer vision which is
an extension of AI and cognitive
neuroscience is the field of building
computer algorithms to automatically
understand the contents of images by
collecting a training data set of images
and hand labeling each image
appropriately you can use ml algorithm
to work out which patterns of pixels are
relevant to your recognition task and
which are nuisance factors information
retrieval information retrieval systems
provide access to millions of documents
from which users can recover any one
document by providing an appropriate
description algorithms that mine
documents are based on machine learning
these learning algorithms use examples
attributes and values which information
retrieval systems can supply in
abundance fraud detection machine
learning aids financial leaders to
understand their customer transactions
better and to rapidly detect fraud
it helps in extracting and analyzing a
variety of different data sources to
identify anomalies in real time to stop
fraudulent activities as they happen
you've learned what machine learning is
and its applications now let's take a
look at the role of machine learning in
SPARC the scalable machine learning
library of SPARC is called ml Lib that
contains general learning utilities and
algorithms including regression
collaborative filtering classification
clustering dimensionality reduction and
underlying optimization primitives you
will learn about some of them in the
following screens SPARC excels at
iterative computation enabling ML Lib to
run fast there are two types of API
firstly a primary API which is the
original spark ml Lib secondly there is
a high-level API which is the pipeline's
spark ml spark 1.2 includes a package
called spark ml which aims to provide a
uniform set of high-level api's that
help users create and tune practical
machine learning pipelines you will
learn about the steps followed in the
general machine learning pipeline in the
next screen here are the steps that are
included in the general machine learning
pipeline the first step is data
ingestion
when a user ingests data it may not be
in the correct form and may require some
cleaning up and transformation which is
the second step this data is an input
for the machine learning algorithm in
the third step the data goes through the
model training the next step is the
model testing phase in the final step
the model is then deployed and
integrated the model feedback then goes
back to the users and reflects in the
user behavior during data ingestion
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