![]() These inferences can be obvious, such as “since the sky is blue today, it will most likely be blue tomorrow.” To that end, learning algorithms function on the basis that strategies, algorithms, and interpretations worked well in the past, so they’re likely to continue working well in the future. ![]() Data mining is another related field of study, focusing on exploratory data analysis through unsupervised learning. The study of mathematical optimization provides methods, theory, and application domains to ML. Some implementations of ML use data and neural networks in a way that mimics the working of a biological brain. ML is related to computational statistics, which focuses on making predictions using computers, but not all ML is statistical learning. This sub-field aims to create computer models that exhibit intelligent behaviors similar to humans, meaning they can recognize a visual scene, understand a text written in natural language, or perform an action in the real world. So much so that the terms are often used interchangeably and sometimes ambiguously as an all-encompassing form of AI. When companies employ artificial-intelligence programs, chances are they’re using machine learning. It’s also behind chatbots and predictive text, language-translation apps, and even the shows and movies recommended by Netflix. Such algorithms are used in myriad applications, including medicine, autonomous vehicles, speech recognition, and machine vision, where it’s difficult or unfeasible to utilize traditional algorithms to perform the required tasks. ML algorithms build a model based on sample data, or training data, to make predictions or decisions without being programmed to accomplish any given task. It’s a branch of artificial intelligence (AI) based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. Machine learning (ML) is a method of data analysis that automates analytical model building. Learn about types of supervised and unsupervised machine-learning approaches. ![]()
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