Supervised machine learning requires the data scientists to provide input and output data, with the goal of the algorithm eventually predicting the correct outputs based on the given input. Unsupervised machine learning, on the other hand, does not require labels and corresponding outputs to be provided. The most common approaches to machine learning training are supervised and unsupervised learning -- but which is best for your purposes? Watch to learn more about the differences between supervised and unsupervised machine learning and how each approach is used.
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What is machine learning ➡️ https://searchenterpriseai.techtarget.com/definition/machine-learning-ML?_ga=2.224407123.852648059.1592831664-1400387709.1579793109/?
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