Machine learning is a type of artificial intelligence that automates data processing using algorithms without necessitating the creation of new programs. In other words, machine learning provides data to a computer, and the computer uses that information to analyze future data. For example, Amazon uses machine learning to automatically make recommendations to customers based on past purchases. Computers can process large amounts of data much more quickly than people can, making machine learning more efficient than human decision-making in many situations, including some aspects of corporate training.
For instance, machine learning can support adaptive learning, the personalization of learning experiences using computer-based technology. With machine learning, computers can use data about employees’ past learning experiences and assessments to tailor learning content to each person. Adaptive learning can result in increased retention, engagement and training ROI.
Organizations can also use machine learning to avoid bias in performance evaluation. It is well known that as humans, we have conscious and unconscious biases that impact our behavior and our beliefs about other people. There are, of course, subjective areas of performance that require human thinking and decision-making to evaluate. However, other areas, such as product sales, could rely on machine learning for evaluation, if sufficient data exists for the computer to draw upon. The speed at which computers can process data also means that they can provide feedback when it is most useful – immediately, rather than annually or quarterly.
The corporate knowledge repository is also an opportunity for machine learning to improve results. These digital information sources enable employees to access information at their time of need and from any location. However, learning libraries can easily become overwhelming and even counterproductive if they are not well managed. Training managers can use machine learning like Netflix does: to draw on their user data to make content recommendations. They can also use algorithms to categorize content, making it more searchable.
Machine learning can also be used to make predictions based on past behavior. How many people are likely to enroll in – or drop out of – a particular training program? How much will the program cost? Which learning styles will respond best to which course elements? How engaged will a new employee become? What engagement methods are likely to appeal to him or her? With enough data, we can answer these questions using machine learning.
Best Practices for Machine Learning
- Develop a thorough understanding of the business problem you’re attempting to solve with machine learning.
- Follow data collection best practices to ensure validity and reliability.
- Machine learning methods vary; make sure you select the right one for your project.
- Include all relevant and valid data in the algorithm.
- Don’t over-rely on machine learning. Sometimes, a different approach is better, and sometimes, a combined approach will lead to the best results.
- Remember that correlation does not imply causation.
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