Machine learning algorithms are broadly categorized under four types of learning problems. Supervised learning trains the algorithm based on example sets of input/output pairs. Unsupervised learning uses data that has not been labeled, classified or categorized. Reinforcement Learning seeks the optimal path to a desired result by rewarding improvement. Bias is defined in practical effect as the ability of an algorithm to the training data to the data training the model to the best with minimal error. This is the second installment in a three-part series about machine learning.”]
Source: https://securityintelligence.com/machine-learning-algorithms-are-not-one-size-fits-all/