Machine learning is quickly being adopted by companies in a variety of industries. We often hear about tech companies like Facebook that use machine learning in multiple areas from targeted advertising to photo tagging. However, the uses of machine learning expands past the tech industry, and almost any company in any industry can benefit from implementing these services. Unfortunately, many business leaders do not yet know much about machine learning and, as a result, may be hesitant to pursue these services for their company.

Good news, we’re here to help! We’ve compiled a beginner’s list of five things that every business leader should know about machine learning. Whether you’re interested in implementing machine learning services for your business or are just curious about what machine learning is, this guide will walk you through the basics.

1. Strong algorithms depend on strong data.

Machine learning uses algorithms that continually learn from and make predictions based on data. These algorithms are dependent on strong data, and while it’s possible to have machine learning without advanced algorithms, the algorithms are useless without good data. With good data, the algorithms can progressively improve its performance and predictions by identifying patterns. Additionally, the more data you have, the more complex and sophisticated your algorithm will be.

2. Machine learning is vulnerable to operator error.

Failures and glitches in machine learning systems are often linked to human error despite the fact that such processes are supposed to use minimal human interference. These problems are likely to arise from errors in the handling of the training data. Training data is imperative to the success of a machine learning system because it shapes the algorithm and teaches it how to analyze future data sets. If the training data has poorly labeled features (a.k.a. variables) or if the production data is vastly different than the training data, then the algorithm will not perform as it’s supposed to. Therefore, it is crucial to review the training data and any changes or updates prior to implementing them into the system.

3. Using machine learning doesn’t require expertise in math.

Users do not need to have expertise in math to use machine learning. Having sufficient knowledge in the business domain will suffice. This is because once a system is set up by a machine learning service provider, the algorithms will do the heavy lifting. The predictive analytics from the algorithms will allow you to quickly get value from your data without any manual calculations.

4. Machine learning can create self-fulfilling predictions.

The decision-making of machine learning systems entirely depends upon previously collected data. As such, the way the data is collected can affect the results of an algorithm. While the algorithm itself remains unbiased, it can learn from unintentional biases included in the data sets. Therefore, the new data generated and processed by the system can inadvertently further support the obtained biases.

5. You encounter machine learning processes every day.

Each time you watch a recommended show on Netflix or ask Siri a question, you are receiving results generated by a machine learning system. While these are just a few notable examples, machine learning services are being implemented by a wide range of industries including banking, healthcare and marketing. Even transportation companies are using machine learning to predict equipment failures and improve safety.