Machine learning is the science of teaching machines to make decisions on their own based on data presented without being explicitly programmed to do so. Typically, this is done through studying patterns in the data.
An example of machine learning is image recognition. For example, non-machine learning code may define a stop sign as an object that has an eight-side shape, is red, and has the word STOP on it. With machine learning, the system is simply shown lots of images of stop signs until it’s able to recognize any stop sign.
Within the machine learning field, two of the most basic types of algorithms are supervised learning and unsupervised learning.
Supervised Learning – In supervised learning, the algorithms are designed so that systems are essentially taught what to do in certain situations. After learning what to do, the system will then make decisions based on what it’s learned.
Unsupervised Learning – In unsupervised learning, the algorithms are designed so that the system is designed to take in data, interpret the data on its own and make decisions based on its own interpretations.