Discuss the difference between supervised and unsupervised machine learning Discuss the difference between supervised and unsupervised machine learning Discuss the difference between supervised and unsupervised machine learning Discuss the difference between supervised and unsupervised machine learning. Provide examples of how machine learning is used in health care. Machine Learning Used in Health Care Machine learning refers to the methods or approaches of data analysis that automate analytical model building. Machine learning is a branch of artificial intelligence based on the concept of learning from data through identifying patterns and decision-making processes with minimum human intervention. There is always an iterative facet of machine learning characterized by exposure to new data and independent adaptation. The iterative approach of machine learning mainly involves recognizing patterns from the previous computations to produce repeatable, reliable decisions and outcomes. Machine learning is not a new technological approach or science, but the one that has gained new momentum from the traditional technological practices (Osisanwo et al., 2017). Machine learning applied different algorithms that are characterized by complex mathematical calculations and big data. Click here to ORDER an A++ paper from our Verified MASTERS and DOCTORATE WRITERS: Discuss the difference between supervised and unsupervised machine learning There exist unsupervised and supervised machine learning. The two approaches are always applied in different scenarios and will diverse datasets. In other words, supervised and unsupervised machine learning depends on the types of available datasets (Choudhary & Gianey, 2017). In supervised machine learning, models are developed or trained using the labeled data. In other words, the development of supervised machine learning often involves creating models that should find mapping functions to map out the input variable (X) and the output variable (Y). The function will therefore be computed as Y = f(x). Supervised machine learning can be applied to solve two major types of problems, including regression and classification. Some examples of supervised machine learning algorithms include linear regression for solving linear regression problems, support vector machines for different classification problems, and the random forest for classification and regression problems. In unsupervised machine learning, patterns are inferred from the unlabeled or uncategorized input data. The main objective of unsupervised learning is to determine the patterns and structures from the input data. There is always no need for any form of supervision (Amruthnath & Gupta, 2018). Instead, it identifies the patterns from the data on its own. Examples of unsupervised machine learning include clustering, understanding various customer groups, and customer segmentation on which to build marketing criteria or brands. Order Now
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