Machine Learning: A Constructive Combination of AI, Big Data & Algorithms
It is no big surprise that machine learning/ artificial intelligence has increasingly gained more popularity in the past couple of years. In the digital era, data is rapidly expanding and so is the need of analyzing and understanding this huge data for making right decisions. Big Data arrives as the hottest trend in the IT sector at the moment; machine learning is an incredibly powerful tool to make predictions or calculated suggestions based on large amounts of data. Most common example of machine learning is ecommerce websites algorithms to make product suggestions based upon the choices made by you as a customer.
What is machine learning? In simple terms, Machine learning is the practice of educating a computer about finding patterns and making connections to understand huge amounts of data. So in order to finish a given task, the machine does not use any programming software and instead of doing that it looks at the Big Data and algorithms to find out patterns for accomplishing it. Machine learning enables applications to look for patterns in the data, think and make wise decisions in the future.
Why Machine Learning is Relevant Today?
Machine Learning solutions are serving to a variety of sectors for optimizing organizational processes such as:
Automotive & Manufacturing: Identifying and navigating roads and obstructions in real-time for autonomous driving, Predicting failure and recommending maintenance on vehicle components, predicting outcomes and minimizing R&D costs, optimizing manufacturing costs are some of the tasks performed by machine learning.
Health-Care: Machine learning helps in diagnosing known diseases from scans, biopsies, audio, and other data. It is also useful in predicting personalized health outcomes to optimize recommended treatments. Machine learning can evaluate doctor’s performance and provide outcome-improving feedback.
Finance: Machine learning is extremely useful in identifying fraudulent activities using customer transactions and other relevant data as well as it is quite helpful in discovering new trends, risks and benefits for predictive analytics.
Telecom: Machine learning can predict lifetime value and risk of churn for individual customers. It is used to allocate the resources, make radical personalization for individual prospects, and forecast demand trends.
Retail and E-Commerce: Machine learning is used for product recommendation, price and product optimization, demand and supply optimization, making customer support a better experience in terms of precision in delivering solutions.
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