Machine Learning For Business Leaders
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 are as important as an actionable understanding of finance and financial management.
Machine Learning is a subset of artificial intelligence, where computer algorithms are able to learn from data and modify their behavior accordingly. These algorithms have the ability to identify patterns in data that humans can’t see. Some commercial applications of Machine Learning include speech recognition, facial recognition, self-driving cars, and automatic stock trading.
Machine Learning has been around for decades, but it has only now begun to find its way into businesses. Recently, the number of organizations that are using machine learning has increased tremendously. As a result, there may be some misconceptions about machine learning that need to be clarified.
Machine learning is not just for scientists and computer programmers anymore. The use of machine learning can be applied to all industries and functional areas within an organization. Machine learning can help with customer retention, risk management, fraud detection, inventory management, product innovation, and many other areas.
What machine learning is
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:
- Categorize or catalog like objects
- Predict likely outcomes or actions based on identified patterns
- Identify unknown patterns and relationships
- Detect anomalous or unexpected behaviors
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.
Problems suited to machine learning
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:
- Logical rules are unavailable or insufficient to describe the environment — but actionable rules can be intuited
- Next actions are varied and the best action depends on conditions that cannot be identified in advance
- Understanding why an outcome is suggested is not as important as the accuracy of the outcome
- The data is problematic for traditional analytic methods
Now that you know what machine learning is and how to identify problems that lend themselves to ML solutions, let’s explore the steps to define and conduct an ML project.
How to plan and execute a machine learning project
Well executed ML systems follow these recommended steps:
- Define Problem
- Prepare Data
- Evaluate Algorithms
- Improve Results
- Present Results
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 the expert assistance you will be prepared to spot and sponsor the next, more consequential machine learning opportunity.
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