Machine Learning has proven to be one of the resourceful method of data analytics. Amidst all the hype around Big Data, we keep hearing the term Machine Learning. What exactly is ML? Well, if we go by the google definition, Machine Learning is the method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions.
Diving deep into its advantages,
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· It is helps in easily identifying trends and patterns:
Machine Learning can review large volumes of data and discover specific trends and patterns that would not be apparent to humans. For instance, for an e-commerce website like Amazon, it serves to understand the browsing behaviors and purchase histories of its users to help cater to the right products, deals, and reminders relevant to them. It uses the results to reveal relevant advertisements to them.
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· Automation:
With ML, you don’t need to babysit your project every step of the way. Since it means giving machines the ability to learn, it lets them make predictions and also improve the algorithms on their own. A common example of this is anti-virus software; they learn to filter new threats as they are recognized. ML is also good at recognizing spam.
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· Continuous Improvement:
As ML algorithms gain experience, they keep improving in accuracy and efficiency. This lets them make better decisions. Say you need to make a weather forecast model. As the amount of data, you have keeps growing, your algorithms learn to make more accurate predictions faster.
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· Handling multi-dimensional and multi-variety data:
Machine Learning algorithms are good at handling data that are multi-dimensional and multi-variety, and they can do this in dynamic or uncertain environments.
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· Wide Applications:
You could be an e-tailor or a healthcare provider and make ML work for you. Where it does apply, it holds the capability to help deliver a much more personal experience to customers while also targeting the right customers.
Although, ML needs enough time to let the algorithms learn and develop enough to fulfil their purpose with a considerable amount of accuracy and relevancy. It also needs massive resources to function. This can mean additional requirements of computer power for you. While, Machine Learning can be incredibly powerful when used in the right ways and in the right places where massive training data sets are available, it certainly isn’t for everyone.
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