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The Art of Machine Learning
Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
It helps in extracting meaningful information from raw data; it is an excellent technique of solving complex-data-rich business problems that are not resolved by the traditional approaches like human judgment. According to Arthur Samuel who coined the term “machine learning“ in 1959, it gives “computers the ability to learn without being explicitly programmed.” Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible;
Examples of applications of machine learning include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition and lots more. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results.
Some of the benefits of machine learning that have already become part of our daily lives include surfing the Internet, reading online weather report using speech recognition, and driving a car using GPS navigation among others. Some of the major enterprise functions that have been optimized using machine learning include:
Sales and marketing – You can use machine learning models for predicting which product a customer would like to buy. The inputs include customer profile such as customer activity, recent purchases, as well as personal details. Enterprises can map this information to predict the likelihood of customer response to a specific product and service.
Disaster management – Machine learning algorithms can be implemented in alarm systems in a hazardous environment such as a chemical manufacturing industry. Such algorithms analyze sensor data that includes measurement of air quality, equipment performance, employee productivity, or unusual behavior. This information can help you avoid accidents.
Risk and fraud management – The scope of machine learning in fraud detection is in identifying descriptions of transactions to their likelihood, and indicating whether an ongoing transaction can be fraudulent or not. This technique helps in identifying credit risk, where you can learn about the demographics and payment history details of a loan applicant; this information will assure the financial institution about the likelihood of the applicant defaulting on the given loan.
Efficient transportation – Analyzing and optimizing traffic situations is one of the benefits of applying machine learning tools. You can understand the usual traffic patterns through comprehensive analysis of sensor data, accident history, and road work. Machine learning tools predict delays, traffic jams, or road blocks and in addition, suggest a faster alternative route to public buses, commercial and consumer vehicles.
Machine learning is being incorporated into solutions in every walk of life from home thermostats to health monitoring systems, equipment maintenance, marketing software, etc. In the latest generation of products, machine learning is adding intelligence pretty much everywhere you look, and Data is driving this trend. More data is available than ever before, but tools are needed to take advantage of it. Machine learning allows the computer to learn from data.