Machine Learning Engineer

Below is part of the result for the query "What is the difference between supervised learning and unsupervised learning?".
Supervised learning and unsupervised learning are two of the most common types of machine learning algorithms. The main difference between them is that supervised learning algorithms are trained on data that has both input and output variables, while unsupervised learning algorithms are trained on data that only has input variables.
In supervised learning, the algorithm learns to map the input variables to the output variables. This is done by minimizing a loss function, which measures the difference between the predicted output and the actual output.
In unsupervised learning, the algorithm learns to identify patterns and structures in the data. This is done by finding clusters of data points that are similar to each other…