Machine learning has been a hot topic in IT discussion and news in recent years, but what it actually means for businesses and consumers is not always clear. Often, it seems like it’s merely the newest buzzword to grab investors’ attention, next in line after “the cloud”, “Internet of Things” and “blockchain”. It may be wise to not write it off just yet, though—understanding what machine learning is in theory and practice can highlight some valuable applications to your business.
What is Machine Learning?
Broadly, machine learning is the study and use of algorithms that allow computer systems to effectively perform a given task without explicit instructions. It’s often compared to artificial intelligence, but this can be misleading. An algorithm built on machine learning does not think in the same way that people do; rather, the algorithm is fed “training data” to generalize from. This allows for the construction of a model to apply to real-world data for prediction or pattern recognition. Training can come in various forms, including:
- supervised training, where the data input has an expected output associated and the algorithm develops a generalized model;
- unsupervised training, where groups of data are collated and analyzed for patterns;
- reinforcement training, featuring a dynamic environment and a goal that software agents must pursue.
Business Applications for Machine Learning
Algorithms created by machine learning vary greatly from one task and training set to another, and the field is still rapidly developing. However, the theories at work are already finding practical use. Machine learning tools for businesses are often designed for forecasting market trends or projecting the results of marketing strategies. The massive data volume this can involve makes it a strong application for analysis backed by machine learning algorithms. To account for bias that may develop unexpectedly, top-grade machine learning tools may employ multiple algorithms to reduce potential variance caused by any one algorithm, permit regular monitoring by human analysts to catch emergent bias or make use of meta-learning algorithms that examine prior analysis for potential biases.