Machine learning With Sabyasachi Upadhya

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Machine learning With Sabyasachi Upadhya
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When Amazon recommends a book you would like or
Google predicts your schedule and Pandora
magically creates a playlist suited to your
likes, it is machine learning on Big Data. With
Big Data projected to drive enterprise IT
spending to 100 billion according to Gartner,
Big Data is here to stay, and as a result, more
businesses of every size are getting into the
game. For enterprise organizations Big Data is a
strategic asset. Each customer, partner, or
supplier response or non-response, transaction,
defection, credit default, and complaint provides
the enterprise the experience from which to
learn. From a consumer perspective, every action
performed online, every sales process, product
interaction, prescribed drug, and environmental
anomaly, is being tracked by various sources.
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Only with advanced analytics, and specifically
machine learning, can companies truly tap into
their rich vein of experience and mine it to
automatically discover insights and generate
predictive models to take advantage of all the
data they are capturing. This advanced analytics
technology means that instead of looking into the
past for generating reports, businesses can
predict what will happen in the future based on
analysis of their existing data. The value of
machine learning is rooted in its ability to
create accurate models to guide future actions
and to discover patterns that weve never seen
before.
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Defining Machine Learning

• Machine learning is the modern science of finding
  patterns and making predictions from data based
  on work in multivariate statistics, data mining,
  pattern recognition, and advanced/predictive
  analytics.

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Machine learning methods are particularly
effective in situations where deep and predictive
insights need to be uncovered from data sets that
are large, diverse and fast changing Big Data.
Across these types of data, machine learning
easily outperforms traditional methods on
accuracy, scale, and speed. For example, when
detecting fraud in the millisecond it takes to
swipe a credit card, machine learning rules not
only on information associated with the
transaction, such as value and location, but also
by leveraging historical and social network data
for accurate evaluation of potential fraud.
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Machine learning methods are vastly superior in
analyzing potential customer churn across data
from multiple sources such as transactional,
social media, and CRM sources. High performance
machine learning can analyze all of a Big Data
set rather than a sample of it. This scalability
not only allows predictive solutions based on
sophisticated algorithms to be more accurate, it
also drives the importance of softwares speed to
interpret the billions of rows and columns in
real-time and to analyze live streaming data.
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For those of us who are practicing and developing
machine learning technology, its no longer
sufficient to provide the ability to achieve the
most accurate, fast, and scalable predictive
insights. Ultimately, for machine learning to
impact the world around us in a truly meaningful
way, we have to deliver Machine Learning in a
smarter, more usable form. By enabling not only
the data scientists who have PhDs but also the
business users to tap into the state-of-the-art
machine learning technology, we will truly bring
this technology to the masses and dramatically
accelerate time-to-insight for organizations of
all sizes.
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arning