Machine learning is a type of artificial intelligence in which algorithms learn more as they ingest and interpret more data. Machine learning algorithms are built on training data, examples that fit the criteria for what the algorithm is learning so they can identify patterns in data. This training data is what the algorithm uses to start learning, and then to apply and refine that knowledge as it ingests and analyzes more data points in order to reach a conclusion without being explicitly programmed to reach that conclusion. Machine learning has a variety of applications, including anomaly detection and predictive analytics.
There are several types of machine learning algorithms. Supervised machine learning learns a function by mapping an input to an output based on the learning data. Supervised learning is typically used for tasks such as linear regression and speech recognition. Unsupervised machine learning takes sets of data that only include inputs to find structures such as grouping or clustering. This learning model uses unlabeled data for training, and then identifies what the data has in common. Neural networks use unsupervised learning to perform complex tasks, such as in medical diagnoses and complex pattern recognitions. Semi-supervised learning lies in between supervised and unsupervised, in that it takes a small amount of labeled data and pairs it with a large amount of unlabeled data during training. This can help improve accuracy while reducing costs associated with labeling data. Reinforcement learning is how software decides to take actions in a given environment to arrive at a conclusion. This type of learning is often used for game theory or for self-driving cars. While these are the basic machine learning models, further models, such as deep learning, have arisen that can utilize any of these models.
Machine learning can benefit businesses and organizations in a variety of ways, such as: