Introduction to Machine Learning

Machine Learning is a subset of Artificial Intelligence. It is a method of data analysis that helps in the automation of analytical model building. Machine learning runs on the idea that systems learn from data, identify patterns and make decisions with minimum human involvement.

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Evolution of Machine Learning

Machine Learning was evolved on the basis that computers can automatically learn from data. As the systems come across new data, they independently adapt. They take into account the previous computations and produce reliable results.

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Why is Machine Learning important?

Machine Learning is important because of the affordable storage of increasing volumes and varieties of available data, and cheaper and more powerful computational processing. This infers that it is possible to automatically create models that possibly analyze enormous and more complex data and deliver accurate and faster results. Also, a business or an organization is able to avoid unknown risks and identify opportunities by building such precise models.

With its increased demand, uses and importance, Machine Learning certified is now being considered as the game changer of the coming decade. It has the capability of automating almost everything and if handled right, can bring huge benefits to humanity as a whole.

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Who uses Machine Learning?

Some prominent sectors have understood the importance of managing and analyzing the large amounts of data and thus moved on and embraced to the Machine Learning technology. By handing the data efficiently and gaining insights from it, organizations have become more efficient and competent.

• Financial Services – Financial industries such as banks use machine learning to identify insights and prevent fraud. These insights help them figure out investment opportunities and thus investors identify the best time to trade.
   
• Government – By analyzing data, government agencies identify ways to increase efficiency. Machine Learning also helps in fraud detection and minimizing identity theft.
   
• Health Care – With the increase in the use of wearable devices and sensors that use data to examine a patient’s health, Machine Learning has become popular in the healthcare industry.
   
• Marketing and Sales – Getting recommended for items related to your recent searches or purchases demonstrates the use of Machine Learning that analyses your buying history. This is seen as the ongoing as well as the upcoming technology for these sectors.
   
• Oil and Gas – Machine Learning technology for this industry is used in identifying new energy sources, reviewing minerals in the ground, predicting refinery sensor failure and streamlining oil distribution.
   
• Transportation – Machine learning technology has proved fruitful in aiding delivery companies, public transportation and transportation organizations by making routes more efficient.
   

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Apart from these, there are many other domains that are leveraging the power of Machine Learning such as manufacturing, teaching and law firms, among others.

The possibilities of Machine Learning are extremely transformative. Organizations may soon find themselves dependent on Machine Learning technologies for making decisions based on core business processing and automation.

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