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Machine learning is a method of data analysis that automates analytical model building using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.
While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications that you may be familiar with:
Many of our day-to-day activities are powered by machine learning algorithms, including, Fraud detection, Web search results, Network intrusion detection, Pattern and image recognition, Email spam filtering. On some phones while taking pictures there are boxes that shows as soon as a face is detected that machine learning
Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range.
Machine learning algorithms are often categorized as being supervised or unsupervised. Supervised algorithms require humans to provide both input and desired output, in addition to furnishing feedback about the accuracy of predictions during training.
Unsupervised learning algorithms are used for more complex processing tasks than supervised learning systems.
Facebook News Feed, for example, uses machine learning to personalize each member’s feed. If a member frequently stops scrolling to read or “like” a particular friend’s posts, the News Feed will start to show more of that friend’s activity earlier in the feed.
Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user’s data and use those patterns to populate the News Feed.
Should the member no longer stop to read, like or comment on the friend’s posts, that new data will be included in the data set and the News Feed will adjust accordingly?
Today, the amount of digital data being generated is huge thanks to smart devices and Internet of Things.
Machine learning based models can extract patterns from massive amounts of data which humans cannot do because we either cannot retain everything in memory or we cannot perform obvious/redundant computations for hours and days to come up with interesting patterns.
Machine learning has found major applications in finance, healthcare, entertainment, robotics and many more.
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