Machine Learning has garnered a significant share of recent press coverage in both tech and main street media. It is inextricably intertwined with, and central to, discussion and dialogue on topics ranging from big data in general to Facebook’s threat to privacy, Boston Dynamics creepy robotics, and Google’s exploitation of artificial intelligence for good and ill. As such, it is easy to view machine learning as either sinister or magical – neither of which is true. For today’s business leader, an objective and actionable understanding of machine learning is as important as an actionable understanding of finance and financial management.
In this article we provide an overview of machine learning for business leaders: what it is and how to think about its applicability to your business.
Machine learning (ML) is a data-driven system development paradigm. ML systems leverage data models, data analysis and feedback to define and refine algorithms to improve model accuracy and system results.
ML systems work by analyzing data to detect patterns or by applying predefined rules to:
Different algorithms learn in different ways. But in general, as new data are provided to the ML system the system “learns” and the algorithm’s performance improves over time.
ML, like other software development paradigms is not one-size-fits-all – some approaches are better suited to particular classes of problems and not suitable for others.
Machine learning is particularly suited to problems where:
Now that you know what machine learning is and how to identify problems that lends themselves to ML solutions, let’s explore the steps to define and conduct an ML project.
Well executed ML systems follow these recommended steps:
These steps, while seemingly generic and common to traditional software system development, require the perspective and attention gained from experience with ML system development.
The best way to approach machine learning system development is to work through an ML project end-to-end and cover the key steps with an experienced guide or team. Every step, from loading data, summarizing data, evaluating algorithms, making initial predictions, refining and presenting results is improved by experience – much like an ML system.
Accordingly, your first project should be viewed as a learning process to understand the mechanics of machine learning, calibrate your expectations and provide a perspective for setting expectations, interpreting and presenting results from dynamic, learning systems. After tackling your first project with expert assistance you will be prepared to spot and sponsor the next, more consequential machine learning opportunity.
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