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What does ML Mean

A lot of data is collected in today’s world. The size of the information is due to the advancement and capacity of technology. In particular, millions of data points are generated daily via the internet alone. All this data can be processed and interpreted into useful information for academia and businesses. At the moment, one of the most popular tools used in machine learning (ML).

About Machine Learning

ML is a branch of artificial intelligence. It allows programmers to create programs that can read, learn, and change the pattern of operation in accordance with the data collected. In addition, ML allows an algorithm to remain relevant in a dynamic environment and does not require intervention from humans. In most cases, ML involves some form of a complex line of codes within computers, which allows for the identification of data and prediction based on the data collected. The rise of big data makes ML essential. It gives data scientists and programmers the ability to process big data into intelligent information. Due to its advantages, machine learning can be used in fraud detection and prevention, financing, advertising, and much more.

Applications of ML

As mentioned earlier, ML has several essential uses. Some of the more common applications of machine learning are discussed below.

Decision-Making Help

Decision-making can be a challenging process. This challenge is amplified by unknown parameters when making a decision. With ML, this entire process can become easier due to the ETL (extract, transform, load) of big data. Businesses can use the information from this tool to get invaluable insight when making important decisions. The advantage offered by machine learning is that it uses a large amount of data that transverses several years. In addition, it gives users the ability to make predictions and scenarios that closely conform to reality.

Improves Search Engine Recommendations

Search engine recommendations are essential aspects of a functional search engine. It makes the searching process easier for users by providing relevant suggestions. In most cases, these recommendations are generated by processing millions of user data. The more users search for information, the better the recommendations become. Likewise, ML used with search engines makes use of personal preferences and search histories to deliver personalized suggestions to users. Having this capacity is instrumental for online businesses with integrated search engines.


A chatbot is now a critical tool for enterprises. It allows people to get information systematically. Therefore, people can resolve problems by communicating with machines. Earlier types of chatbots were based on scripts, which provided a fixed answer for a set of keywords and key phrases. Now, machine learning has made chatbots capable of conversing with humans using natural language. This change has significantly improved people’s perception of chatbots. Likewise, it has improved customer satisfaction.

Improved Understanding of Customer Churn

All businesses care about customer churn. With ML, entities are better able to create accurate models of customer churn. By extension, an entity can create winning strategies to reduce customer churn and improve customer retention.

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