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Machine Learning Market valued $XX million in 2019 and is poised to expand with a CAGR of X.X% during the forecast period to reach a market value of $XX million by the end of 2026.

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Machine Learning Market Key PlayersInternational Business Machines CorporationMicrosoft CorporationSAP SESAS Institute Inc.Amazon Web Services, Inc.BigML, Inc.Google Inc.Fair Isaac CorporationBaidu, Inc.Hewlett Packard Enterprise Development LPIntel CorporationH2o.AI

The Machine Learning market report an extensive study of product, application, end users and regions in Machine Learning market worldwide. The report offer global, regional and country level analysis of all market segments with qualitative analysis including market driving forces, growth restraining factors, market trend and growth opportunity analysis. Moreover, the qualitative analysis is supported with facts and figures which sheds light on quantitative aspect of the market.The report provides Machine Learning market value for prominent product and services categories along with their application in various field of the industry. For each segment the report provides market value in terms of USD million for historical period 2016-2019 and future market projections for forecast period 2020-2026.

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The report further dives into regional Machine Learning market where it brings geographic market scenario on surface. The report only provides total regional and country market numbers, but also offers product and application market figures in each country across major regional market. This puts forth a comprehensive perspective of market highlighting fasting growth regional and country level market, fastest growing product market in a particular regions/ country and also present comparative analysis of regional markets Machine Learning products. Machine Learning Market Segmentation:

The Machine Learning market report is segmented into following categories;

By TypeardwareSoftwareServicesBy ApplicationealthcareBFSILawRetailAdvertising & MediaAutomotive & TransportationAgricultureManufacturing

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Above segments market information will be covered for following regions North AmericaThe U.S.CanadaMexicoEuropeGermanyFranceUKItalySpainRussiaAsia PacificChinaJapanIndiaSouth KoreaAustraliaMiddle East and AfricaSaudi ArabiaUAESouth AfricaSouth AmericaBrazilArgentina

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About UsReport Engines is a market research firm operating in business to business and business to customer research. The company provides qualitative and quantitative market research reports covering all major industry domains ranging from technology to manufacturing industry. Our market research expertise are in healthcare, automotive, food and beverages, telecom and IT, energy and power, etc.

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COVID-19 Impact and Recovery Analysis-Machine Learning Market | Growth & Forecast 2020 - Cole of Duty

With healthcare organizations around the world leveraging cloud technologies for key clinical and operational systems, the industry is building toward digitally enhanced, data-driven healthcare.

And unstructured healthcare data, within clinical documents and summaries, continues to remain an important source of insights to support clinical and operational excellence.

But there are countless nuggets of important unstructured data something that does not lend itself to manual search and manipulation by clinicians. This is where automation comes in.

Arun Ravi, senior product leader at Amazon Web Services is copresenting a HIMSS20 Digital presentation on unstructured healthcare data and machine learning, Accelerating Insights from Unstructured Data, Cloud Capabilities to Support Healthcare.

There is a huge shift from volume- to value-based care: 54% of hospital CEOs see the transition from volume to value as their biggest financial challenge, and two-thirds of the IT budget goes toward keeping the lights on, Ravi explained.

Machine learning has this really interesting role to play where were not necessarily looking to replace the workflows, but give essentially a superpower to people in healthcare and allow them to do their jobs a lot more efficiently.

In terms of how this affects health IT leaders, with value-based care there is a lot of data being created. When a patient goes through the various stages of care, there is a lot of documentationa lot of datacreated.

But how do you apply the resources that are available to make it much more streamlined, to create that perfect longitudinal view of the patient? Ravi asked. A lot of the current IT models lack that agility to keep pace with technology. And again, its about giving the people in this space a superpower to help them bring the right data forward and use that in order to make really good clinical decisions.

This requires responding to a very new model that has come into play. And this model requires focus on differentiating a healthcare organizations ability to do this work in real time and do it at scale.

How [do] you incorporate these new technologies into care delivery in a way that not only is scalable but actually reaches your patients and also makes sure your internal stakeholders are happy with it? Ravi asked. And again, you want to reduce the risk, but overall, how do you manage this data well in a way that is easy for you to scale and easy for you to deploy into new areas as the care model continues to shift?

So why is machine learning important in healthcare?

If you look at the amount of unstructured data that is created, it is increasing exponentially, said Ravi. And a lot of that remains untapped. There are 1.2 billion unstructured clinical documents that are actually created every year. How do you extract the insights that are valuable for your application without applying manual approaches to it?

Automating all of this really helps a healthcare organization reduce the expense and the time that is spent trying to extract these insights, he said. And this creates a unique opportunity, not just to innovate, but also to build new products, he added.

Ravi and his copresenter, Paul Zhao, senior product leader at AWS, offer an in-depth look into gathering insights from all of this unstructured healthcare data via machine learning and cloud capabilities in their HIMSS20 Digital session. To attend the session, click here.

Twitter:@SiwickiHealthITEmail the writer:bill.siwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

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Machine learning can give healthcare workers a 'superpower' - Healthcare IT News

TLDR: The Complete 2020 Big Data and Machine Learning Bundle breaks down understanding and getting started in two of the tech eras biggest new growth sectors.

Its instructive to know just how big Big Data really is. And the reality is that its now so big that the word big doesnt even effectively do it justice anymore. Right now, humankind is creating 2.5 quintillion bytes of data every day. And its growing exponentially, with 90 percent of all data created in just the past two years. By 2023, the big data industry will be worth about $77 billion and thats despite the fact that unstructured data is identified as a problem by 95 percent of all businesses.

Meanwhile, data analysis is also the background of other emerging fields, like the explosion of machine learning projects that have companies like Apple scooping up machine learning upstarts.

The bottom is that if you understand Big Data, you can effectively right your own ticket salary-wise. You can jump into this fascinating field the right way with the training in The Complete 2020 Big Data and Machine Learning Bundle, on sale now for $39.90, over 90 percent off from TNW Deals.

This collection includes 10 courses featuring 68 hours of instruction covering the basics of big data, the tools data analysts need to know, how machines are being taught to think for themselves, and the career applications for learning all this cutting-edge technology.

Everything starts with getting a handle on how data scientists corral mountains of raw information. Six of these courses focus on big data training, including close exploration of the essential industry-leading tools that make it possible. If you dont know what Hadoop, Scala or Elasticsearch do or that Spark Streaming is a quickly developing technology for processing mass data sets in real-time, you will after these courses.

Meanwhile, the remaining four courses center on machine learning, starting with a Machine Learning for Absolute Beginners Level 1 course that helps first-timers get a grasp on the foundations of machine learning, artificial intelligence and deep learning. Students also learn about the Python coding languages role in machine learning as well as how tools like Tensorflow and Keras impact that learning.

A training package valued at almost $1,300, you can start turning Big Data and machine learning into a career with this instruction for just $39.90.

Prices are subject to change.

Read next: The 'average' Robinhood trader is no match for the S&P 500, just like Buffett

Read our daily coverage on how the tech industry is responding to the coronavirus and subscribe to our weekly newsletter Coronavirus in Context.

For tips and tricks on working remotely, check out our Growth Quarters articles here or follow us on Twitter.

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Big data and machine learning are growing at massive rates. This training explains why - The Next Web

At Pew Research Center, we collect and analyze data in a variety of ways. Besides asking people what they think through surveys, we also regularly study things like images, videos and even the text of religious sermons.

In a digital world full of ever-expanding datasets like these, its not always possible for humans to analyze such vast troves of information themselves. Thats why our researchers have increasingly made use of a method called machine learning. Broadly speaking, machine learning uses computer programs to identify patterns across thousands or even millions of data points. In many ways, these techniques automate tasks that researchers have done by hand for years.

Our latest video explainer part of our Methods 101 series explains the basics of machine learning and how it allows researchers at the Center to analyze data on a large scale. To learn more about how weve used machine learning and other computational methods in our research, including the analysis mentioned in this video, you can explore recent reports from our Data Labs team.

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What is machine learning, and how does it work? - Pew Research Center

Last Friday, the USs Dow Jones Index climbed up by almost 1,000 points. The U.S. Labor Department said that the economy unexpectedly added 2.5 million jobs in May. This followed a depressing April, when the country shed as many as 20 million jobs. This lowered the unemployment rate to roughly 13%, versus the 15% it had hit in April. The report also surprised economists and analysts who had forecast millions more losing their jobs. Their Machine Learning (ML) models were predicting that the jobless rate would continue to rise to over 20%.

This isnt the first time that the technology around ML has failed. In 2016, sophisticated ML algorithms failed to predict the outcomes of both the Brexit vote as well as the US presidential election. Some make the argument that algorithm-driven machine prediction was in its infancy in 2016. If thats the case, then what have the intervening four years of computer programming and an explosion of data available to train" deep-learning algorithms really achieved?

As a concept, ML represents the idea that a computer, when fed with enough raw data, can begin on its own to see patterns and rules in these numbers. It can also learn to recognize, categorize and feed new data upon arrival into the patterns and rules already created by the computer program. As more data is received, it adds to the intelligence" of the computer by making its patterns and rules ever more refined and reliable.

There is still a small but pertinent inconvenience that deserves our attention. Despite the great advances in computing, it is still very difficult to teach computers both human context and basic common sense. The brute-force approach of Artificial Intelligence (AI) behemoths does not rely on well-codified rules based on common sense. It relies instead on the raw computing power of machines to sift thousands upon thousands of potential combinations before selecting the best answer using pattern-matching. This applies as much to questions that are intuitively answered by five-year-olds as it does to a medical image diagnosis.

These same algorithms have been guiding decisions made by businesses for a while nowespecially strategic and other shifts in corporate direction based on consumer behaviour. In a world where corporations make binary choices (either path X or path Y, but not both), these algorithms still fall short.

The pandemic has exposed their insufficiency further. This is especially true with ML systems at e-commerce retailers that were initially programmed to make sense of our online behaviour. During the pandemic, our online behaviour has been volatile. News reports in various Western countries that kept e-commerce alive during their lockdowns have focused on retailers trying to optimize toilet paper stocks one week and stay-at-home board games the next.

The disruption in ML is widespread. Our online buying behaviour influences a whole hoard of subsidiary computer systems. These are in areas such as inventory and supply chain management, marketing, pricing, fraud detection and so on.

To an interested observer, it would appear that many of these algorithms base themselves on stationary assumptions about data. A detailed explanation of how stationary processes are used for statistical data modeling and predictions can be found here. Very simply put, this means that algorithms assume that the rules havent changed, or wont change due to some event in the future. Surprisingly, this goes against the basic admonition that almost all professional investors bake into their fine print, especially the one that says, Past performance is no predictor of future performance."

The paradox is that finding patterns and then using them to make useful predictions is what ML is all about in the first place. But static assumptions have meant that the data sets used to train ML models havent included anything more than elementary worst case" information. They didnt expect a pandemic.

Also, bias, even when it is not informed by such negative qualities as racism, is often added into these algorithms long before they spit out computer code. The bias enters through the manner in which an ML solution is framed, the presence of unknown unknowns" in data sets, and in how the data is prepared before it is fed into a computer.

Compounding such biases is the phenomenon of an echo chamber" that is created by finely-targeted algorithms that these companies use. The original algorithms induced users to stay online longer and bombarded them with an echo-chamber overload of information that served to reinforce what the algorithm thinks the searcher needs to know. For instance, if I search for a particular type of phone on an e-commerce site, future searches are likely to auto-complete with that phone showing up even before I key in my entire search string. The algorithm gets thrown off when I search for toilet paper instead.

The situation brought about by the covid pandemic is still volatile and fluid. The training data sets and the computer code they produce to adjust predictive ML algorithms are unequal to the volatility. They need constant manual supervision and tweaking so that they do not throw themselves and other sophisticated downstream automated processes out of gear. It appears that consistent human involvement in automated systems will be around for quite some time.

Siddharth Pai is founder of Siana Capital, a venture fund management company focused on deep science and tech in India

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Opinion | Covid has exposed the limitations of machine learning - Livemint