Machine learning continues to grow in importance for many industries; here’s what your business should know about it.
Beginner or expert, make sure you know these things about machine learning algorithms.
Machine learning has dominated tech news as of late – and with good reason. Machine learning applications help businesses gather data, decipher and analyze the information, and learn from it. Machine learning algorithms allow for better predicting of patterns and behaviors and are instrumental in a number of industries, namely financial services, information technology, marketing, and manufacturing.
Whether you are beginning the process of figuring out if machine learning tools are a good fit for your business, or you’re simply curious about how machine learning is benefitting other businesses, the first of machine learning basics you should understand is that all machine learning is driven by algorithms. From what Facebook chooses to show you when you sign into your account to the programming that drives stock market investing software, they all share the same core – mathematical algorithms that decide what information to prioritize and use to predict next steps. Here is a list of machine learning algorithms that help drive many machine learning projects.
SAS defines machine learning, its algorithms, and importance for businesses as “analytical models allow researchers, data scientists, engineers, and analysts to produce reliable, repeatable decisions and results and uncover hidden insights through learning from historical relationships and trends in the data.” Most experts would break common machine learning algorithms into the following categories:
This type of statistical machine learning includes labeled data and is a predictive model algorithm. As the name implies “supervised” learning requires a certain amount of human input. It is a useful algorithm for classification, as well as regression. You can find this kind of machine learning example in anything that uses machine learning to predict values, like costs of houses in a neighborhood or credit scores.
This kind of machine learning uses unlabeled data. The website, Towards Data Science, says simply, “These algorithms a particularly useful in cases where the human expert doesn’t know what to look for in the data.” These are the algorithms that tend to be behind pattern detection as well as analyzing and summarizing large groups of data points.
Reinforcement learning is the one style of machine learning that we most closely understand as AI. The algorithm makes decisions based on the data that it gathers, as opposed to just feedback or predictions. Some applications include self-driving cars and sophisticated online games, like chess against an AI opponent.
While your business may not be looking to create the next self-driving car, understanding the basics of machine learning can still be important to understanding how it can benefit your business or help you interact with your consumers and the masses of data that you likely gather about them, their preferences, and behaviors. The importance of machine learning in business will continue to grow as AI and deep learning technologies become more sophisticated and make business processes easier and more intuitive.