You’ve heard of machine learning and seen what it can do, but how exactly do machines learn? The short answer: Algorithms. We feed algorithms, which are sets of rules used to help computers perform problem-solving operations, large volumes of data from which to learn. Generally, the more data a machine learning algorithm is provided the more accurate it becomes.
Machine learning algorithms are split into two main categories based on how they interact with data: Supervised and unsupervised. Due to their differences when analyzing data, these two machine learning categories are better suited for solving different problems. All forms of machine learning rely on the availability of a huge quantity of data to train algorithms. In the infographic below you’ll see how both supervised and unsupervised ingest this data and which problems they are suited for solving.
