Introduction to machine learning: Giving rise to the world of smart technologies

What is machine learning? 

Machine learning is a mechanism that involves data analysis by utilizing automated analytical model building. It works on the chief idea that systems are capable of learning from data, identify odd patterns and make profound decisions with little to no human intervention. 

How did machine learning evolve? 

With the advancement in novel computer technologies, machine learning has modified and it’s not similar to how it used to appear in the past. It came into existence from the age-old theory that computers have the ability to learn some particular functions without having to program, the researchers in the field of AI were keen to know whether computers could learn from existing data or not. It is noticed that when models are exposed to new forms of data, then they have independent adaptability, which makes the aspect of machine learning quite important. Moreover, they get to learn from previous computations, for producing decisions  that are reliable, repeatable and provide surefire outcomes. It has gained new momentum with the ongoing trends and advancements, it’s still an old concept in science. 

Various machine learning algorithms have worked in the past, but the recent development is having the capability to automatically apply complicated mathematical calculations to big data, and that too repeatedly, at a faster pace than ever. Some examples of machine learning applications can be stated as follows: 

  • The powerful and automatized self-driving Google car
  • Regular online recommendations from sites like Amazon, Netflix
  • Get to know what customers say about you on Twitter 
  • Detection of fraud( most usable feature) 

Importance of machine learning:

More and more researchers are keen in the branch of machine learning because of the same reason that pulled them towards data mining and Bayesian analysis. Machine learning involves increasing the volume and accuracy of data, cheap and powerful computational processing and better yet affordable storage of data. Therefore, it gives rise to the strong possibility of quicker and automated models that perform analysis on bigger and complex datasets, enables faster delivery and brings effective outcomes, even on a large scale. Certain data preparation capabilities, basic and advanced algorithms, automotive and iterative processes, scalability and lastly ensemble modeling contributes to a good machine learning system. 

Use of machine learning in different sectors of economy: 

 

  • Financial services: The chief purposes for which banks or businesses in the financial sector use machine learning are: identification of essential insights in datasets and prevention of fraud. With the help of insights, identification of investment opportunities or knowing the right time to trade, is possible. Moreover, the data mining technology can help in identification of clients having high-risk profiles or utilizing cybersurveillance for pointing out warning signs of any fraud that may occur. 

 

  • Government: As the government agencies like public safety and utilities possess various sources of data for enabling data mining, they are in utmost need of machine learning. The proper study of sensor data that lies with the government, more often it is helpful in identifying ways to improve efficiency and lessen expenditure. As with the financial services, machine learning can do the detection of fraud and lower identity theft even while operating for the government agencies.

 

  • Healthcare: With the advent of wearable technology like devices and sensors that are used for assessing a patient’s health in real time, machine learning has become a powerful trend in the healthcare industry. With the help of machine learning technologies, the medical professionals can easily detect any red flags or prepare for diagnosis and treatment. Machine learning is catering to healthcare in the development and growth of the care delivery system.

 

  • Retail:Machine learning is used to analyze the buying history and depending on that, it recommends items that the consumer might like, a commendable use of machine learning. Nowards, retailers depend on machine learning for capturing data, analyzing data sets and using them to create a shopping experience, implementation of a marketing campaign, optimization of price, merchandise supply planning and gaining customer insights. It is providing all the expert services to the retail industry for better growth and development. 

 

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