Create machines that can learn With computer

science and statistics

What it means to muster a subject?

When we say we have learned a subject well, what we mean is that we can now go beyond reciting facts that we took notes and can now apply our in-depth knowledge of the subject to solve bigger more serious problems concerned with the subject.

Scientists have long been challenged to convert the routine basic calculator devices into the more nuanced understanding super computers, to robots powered with magical AI.

It was just a decade ago when

Things started to unfold and materialize…

In the realm of machine learning

The gospel of machine learning started just a decade ago…

A decade from now, the researchers’ dream of algorithms, flexible with the capability of evolving became a reality and came to be known as machine learning.

It was now a practice from a theory. Now machine learning is a part of our daily lives with automated trading strategy-making websites used by Wall Street or the language translation website, sites underlying advanced searching and much more.

Scientists have long tried to form a bridge between two disciplines

Statistics and computer science. So, that they expand the reaches of machine learning and develop

better theories and methods.

That way making accurate predictions an extracting

insight from chaotic data will become much easier.

These days computer science is growing more focused towards

data than on computation

And acting as a great counterpart statistics also

requires advanced computational applications. Thus, both these disciplines

need each other to move forward.

Sophisticated computation is necessary to drive large data sets

The bridge between these two separate fields is Machine Learning which draws on both these subjects and also pushes both of these forward.

Currently the main focus of machine learning is to develop algorithms and computer programs that can work with minimum human input, and extract information from large sets of unorganized numbers, audios, videos or texts.

The main aim being that computers will be able to make predictions or decisions which

have not been coded in its instruction.

Newer avenues are opening up in field of applied mathematics like never before, with the emergence of Big

Data.

The importance of developing machine learning

Big Data application is growing more and more common in a variety of fields like biology, astronomy, humanities and much more. That is why scientists need improved statistical technology to filter out the meaningful signals from the noise.

Machine learning if harnessed up to its optimal powers can do much more wonders than just, face to speech recognition and cars that can drive themselves. Now even drones are available in the market than can be used as a personal device which self-drive themselves.

Moreover, scientists are also working towards

applying these techniques to personalized medical

treatments.

The potential impact of machine learning is unimaginably large.

The ideas researchers are coming up may be applied in

ways that cannot even be anticipated right now.

Join the ranks of statisticians driving this

change and be a data scientist

For more information of SAS applications and courses, visit

http://www.dexlabanalytics.com/