Collage: Vassily Kandinsky, Soft Hard /machine learning
This article, authored by Cory Popescu, SIP Writers Forum, is for the IT / Internet professional, but it is equally applicable to anyone, as we use computer and mobile devices in all aspects of our lives regardless of our profession.
As humans delegate more tasks and responsibilities to machines, machine learning becomes integral part in structuring and remodelling daily business while combining both novel and traditional business approach. Generally, machine learning represents an application of rules and algorithms which delivers meaning to the analyzed data proposing a solution for the end user.
The Internet presents numerous and robust possibilities for the new programs, apps, devices that embed machine learning allowing the accessing of both data and the tools needed to generate the proper solutions to specific audiences.
Depending on the strategy selected, the machines with learning capability can employ unsupervised learning which allows the devices to use different types of algorithms and receive no feedback. These algorithms can also be relatively easier to find on the Internet collections of algorithms and /or structures.
The Internet can help machine learning connect with the Internet data-sets in an effortless manner due to a variety of reasons, such as:
- Direct communication through networks and not a central hub: as inherently known, communication across the networks and between two points becomes possible forthright and open using the internet. In some cases, web sites protection disallows contact. However the connection can be established through sets of privileges and permissions and the end result enables the findings of relevant and specific data.
- Extensive volume of data and high amount of players on the internet who generate and create large databases. These data sets can be accessed either randomly or non-randomly through a variety of devices which include collections of complex algorithms. The algorithms applied in machine learning are data-driven. Through machine learning, the devices access and examine huge databases so that, for example, they can monitor and optimize the performance of industrial equipments.
- Particular processes of machine learning, from non-random examples, based on cases exposed on the internet. The more cases, the better, therefore more adequate solutions appear. Since the processes include the use of algorithms, it implies operational accuracy to analyze and interpret the data. Being processed by machines, the huge batches of data do not represent an issue in part due to dramatically increased computational speeds and also due to the approach to data selection.
- Complexity and diversity of internet searches and novel ways to find the appropriate tools required. Through the machine learning, series of algorithms and methods apply to representative data sets in order to derive relevant meaning for specific results. The internet searches have improved continuously and significantly. One criteria is represented by how fast Google indexes web site pages and updates the additional information found in these indexes. For the past several years this indicator has increased more than 50,000 times. Another criteria represents how fast the visitors can get their responses when accessing the web pages. Over the recent several years, this has increased at least seven times which implies that faster responses, more performant and more computers exist in the Internet's networks.
UCLA: Center for Digital Humanities: Machine learning, stories and the Internet
KDnuggets: The 10 Algorithms Machine Learning Engineers Need to Know
Google: Search Console Help: Webmaster Guidelines
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