Human beings learn through experience. Imagine being stung by a bee. You feel a sharp pain. Consequently, you learn that bee stings hurt and are undesirable. However, this information does not tell you how to avoid bee stings. You may notice that the first time a bee stung you, you were trying to move a honeycomb. The next time it stings, you are cutting flowers. You discuss your experiences with other people and find out that they were also stung when they tried to move a nest or cut flowers. Determine that these actions increase the likelihood that you will be stung by a bee.
Machine learning uses a similar learning process. Artificial intelligence (AI) enables computer systems to learn from experience. The systems use the knowledge gained to improve their operations. These systems do not receive new programming from an external source. Instead, machine learning is an internal process that allows computer systems to evolve through acquired knowledge without human intervention. Let’s explore some of the benefits of machine learning.
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1. Machine learning makes it easy to acquire large amounts of data.
The advantages of machine learning are the fact that it facilitates the acquisition of new data. With machine learning, computer systems constantly acquire new data as they operate. Computer systems use the data to make predictions and identify practical applications to improve their operations.
Acquiring raw data can be complicated and expensive when left to humans. Statisticians and data scientists can create surveys or questionnaires to collect data. They review each data set and identify patterns, drawing ideas from the acquired data.
However, when humans collect large amounts of data, they can collect irrelevant data. Information provided by external sources may be inaccurate or not applicable to your purpose. Humans must delete irrelevant data, which can take a long time. Ignoring irrelevant information can undermine your data analysis and conclusions. A clear advantage of machine learning is that algorithms allow AI systems to process relevant data without the risk of human error.
2. Machine learning identifies patterns and trends.
Machine learning algorithms are ideal for pattern recognition. Computer systems constantly acquire new information and use predictive analytics to determine probable outcomes from the data. Let’s say you buy a new computer from Amazon. Amazon tells you that other people who made similar purchases also bought a mouse pad and computer desk based on this purchase. Amazon is using data analysis of other buying trends to try to sell you other products.
Companies use this information not only to determine what you will buy, but when you will. Let’s say Amazon determines that you buy a bag of dog food once every five weeks. Amazon may remind you that it might be time to buy more dog food.
3. The applications are endless.
Machine learning has no limits. AI systems can review historical data or new data. They can apply deep learning strategies and use big data to decompose large data sets to process large amounts of data. Machine learning can be applied in multiple fields. Product manufacturers use predictive analytics to determine the level of product sales. Meteorologists can process larger amounts of historical data to fine-tune weather forecasts and determine how weather patterns will behave.
For consumers, machine learning enables your streaming services to identify shows you might like based on your viewing history. Helps email filters remove irrelevant messages. It is an integral part of speech and image recognition. Machine learning can even identify potential fraud, alerting banks and credit cards to suspicious activity to protect consumers.
Machine learning has multiple practical applications. Government industries, businesses, and individual consumers all benefit from machine learning.