More and more frequently, business leaders are confronted with exceedingly complex concepts that have the potential to revolutionize how the business operates. Yet, business leaders rarely have time to dive deep into every topic, let alone integrate every new practice or technology into their organization.
Machine learning is a thrilling field within artificial intelligence that already has dozens of business applications. Major organizations already utilize machine learning tools and services to improve their processes in a variety of ways, and the sooner business leaders apply machine learning, the better. Fortunately, business leaders unfamiliar with machine learning can understand the essentials with the following facts:
Machine Learning Models Learn Like the Human Brain
For almost all of digital history, computers have operated on the strict instructions of their programming code, unable to deviate from a specific path. With the development of machine learning algorithms, computer programs gain the ability to adapt their processes when provided with new data, gaining knowledge similar to how humans do.
In fact, the first computer scientists to speculate about machine learning drew upon theoretical models for how neurons grow within the human brain. The human brain learns by establishing connections between neurons that are frequently used in conjunction with one another. Machine learning programs simulate this biological process with digital nodes, which relate to other nodes using positive or negative values. A machine learning tool can increase or decrease those values as it acquires more data. The networks of nodes within machine learning applications are called neural networks.
Machine Learning Models Require Training
Just as people require education to become knowledgeable and skilled, machine learning tools must be trained with data to establish a foundation from which they can grow their understanding and ability. In some neural networks, training is as simple as uploading a dataset to the machine learning model, which will allow the model to create and connect nodes. However, neural networks benefit from frequent testing and adjustment, which can guide the tool toward more accurate results.
There are different types of neural networks, which are used for different kinds of processes. For example, a feedforward neural network is ideal for natural language processing, while convolutional neural networks are better for helping computers recognize visual patterns in images and videos. It is important for a business to use the right kind of neural network to support their machine learning tool; otherwise, they won’t be able to train their tool to accomplish the tasks they require.
The Goal of Machine Learning is Optimization
For many decades, the only goal computer scientists had for the field of machine learning was to find out if it was possible for a machine to learn. Now that executives can apply machine learning in business, there is a new goal for these tools: optimization.
Machine learning programs help business leaders optimize various processes within their operations. The most widely accessed applications for machine learning in business operations involve recommendation engines, which optimize digital advertising to target consumers most likely to engage with a company’s content or products. Machine learning tools can be instrumental in optimizing fraud detection and cybersecurity, reducing business expenditures related to these threats. Businesses can take advantage of machine engine modeling tools, like churn modeling or customer lifetime value modeling, which help hone business strategies. Every day, leaders find new and innovative ways to use machine learning to improve the efficiency of all sorts of business operations.
Machine Learning Is Only as Good as Its Data
There is one minor drawback to machine learning: It can generate inaccurate results. If machine learning is trained with imbalanced data, it is likely to develop biases that impact the reliability of its outputs. Fortunately, it is entirely within the power of business leaders, or their data science teams, to aggregate consistent and true data for training their machine learning tools. Additionally, it is important to recognize that training a machine learning model is not a one-and-done endeavor; business leaders and their teams should monitor the performance of their tools in the real world, where unexpected challenges can arise, and correct any issues as they arise. Leaders can enroll in a course dedicated to machine learning in business to better understand how to leverage data properly to make the most of their machine learning tools.
Most business leaders do not need to learn how to create a neural network from scratch or how to input and export data from a machine learning tool. As long as business leaders understand the purpose of machine learning, they can find ways to implement this revolutionary technology to the benefit of their organizations.
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