Machine Learning is a section of Computer Sciences which empowers Computers to learn on their own by looking at past data without being explicitly programed to perform a function. Genesis of Machine Learning lies in study of Pattern Recognition and Computational Learning Theory in artificial intelligence. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Machine Learning algorithms overcome following strictly static program instructions by making data driven predictions or decisions.
There are quite a few applications that are very difficult to program and should be left best to the machine learner programs, successful approach will be to write an algorithm for self learning. The examples of such applications can be handwriting recognition programs, Natural Language Processing engines, etc. Machine learning has made significant inroads into Data Mining practices and is employed in range to computing tasks where coding static programing instructions is either difficult or just not feasible.
Machine Learning algorithms can be employed to effectively monitor and flag both internal and external threats. The program can be trained to detect usage/access patterns in the corporate LAN or the Cloud, report anomalies that could predict security breaches. The pattern recognition programs can also alert the system administrators against probable outages before they happen.
The Machine Learning programs can be used to flag suspicious behaviour of individuals in busy places like Airports/Railway Stations/public gatherings. These programs can detect things that the human screeners can miss and also help in avoid false alarms as they speed up the screening processes.
Machine learning algorithms can process more information and spot more patterns than their human counterparts. Computer assisted diagnosis (CAD) can be used to detect patterns in the diagnostic Lab reports and predict deceases in patients before they have happened. This can help Doctors to adopt preventive therapy for such patients. Additionally, machine learning can be used to understand risk factors for disease in large populations.
The more you can understand about your customers, the better you can serve them, and the more you will sell. That’s the foundation behind marketing personalisation. Perhaps you’ve had the experience in which you visit an online store and look at a product but don’t buy it — and then see digital ads across the web for that exact product for days afterward. That kind of marketing personalization is just the tip of the iceberg. Companies can personalise which emails a customer receives, which direct mailings or coupons, which offers they see, which products show up as “recommended” and so on, all designed to lead the consumer more reliably towards a sale.
Machine learning is getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money laundering. The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.
You’re probably familiar with this use if you use services like Amazon or Netflix. Intelligent machine learning algorithms analyze your activity and compare it to the millions of other users to determine what you might like to buy or binge watch next. These recommendations are getting smarter all the time, recognizing, for example, that you might purchase certain things as gifts (and not want the item yourself) or that there might be different family members who have different TV preferences.
Perhaps the most famous use of machine learning, Google and its competitors are constantly improving what the search engine understands. Every time you execute a search on Google, the program watches how you respond to the results. If you click the top result and stay on that web page, we can assume you got the information you were looking for and the search was a success. If, on the other hand, you click to the second page of results, or type in a new search string without clicking any of the results, we can surmise that the search engine didn’t serve up the results you wanted — and the program can learn from that mistake to deliver a better result in the future.
NLP is being used in all sorts of exciting applications across disciplines. Machine learning algorithms with natural language can stand in for customer service agents and more quickly route customers to the information they need. It’s being used to translate obscure legalese in contracts into plain language and help attorneys sort through large volumes of information to prepare for a case.
IBM recently surveyed top auto executives, and 74% expected that we would see smart cars on the road by 2025. A smart car would not only integrate into the Internet of Things, but also learn about its owner and its environment. It might adjust the internal settings — temperature, audio, seat position, etc. — automatically based on the driver, report and even fix problems itself, drive itself, and offer real time advice about traffic and road conditions.
In todays landscape, the information derived from the OBD, using OBD port reader, can also be used to recommend usage patterns by the customer to maximise driving efficiency like millage, minimize wear and tear, etc.
An Application Programming interface is a set of subroutine definitions, protocols, and tools for building application software.
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