supervised and unsupervised learningand common algorithmic approaches in machine learningincluding the -nearest neighbor algorithmdecision tree learningand deep learning we'll explore which programming languages are most used in machine learningproviding with some of the positive and negative attributes of each additionallywe'll discuss biases that are perpetuated by machine learning algorithmsand consider what can be kept in mind to prevent these biases when building algorithms machine learning methods in machine learningtasks are generally classified into broad categories these categories are based on how learning is received or how feedback on the learning is given to the system developed two of the most widely adopted machine learning methods are supervised learning which trains algorithms based on example input and output data that is labeled by humansand unsupervised learning which provides the algorithm with no labeled data in order to allow it to find structure within its input data let' explore these methods in more detail supervised learning in supervised learningthe computer is provided with example inputs that are labeled with their desired outputs the purpose of this method is for the algorithm to be able to "learnby comparing its actual output with the "taughtoutputs to find errorsand modify the model accordingly supervised learning therefore uses patterns to predict label values on additional unlabeled data