Introduction to Machine Learning
Machine learning is changing the ways businesses make decisions, get work done, and interact with customers. It involves designing computer systems so they learn through experience to perform tasks and improve their performance. Instead of being explicitly programmed, this kind of system learns from a large data set. Then it uses patterns or characteristics it has identified to analyze new information.
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Companies use machine learning for diverse purposes. It's all about finding patterns and automating the extraction of value from data in order to get the right information, to the right person, at the right time. In service businesses, machine learning can be used to help forecast demand, optimize pricing, and personalize the customer's experience.
In manufacturing, machine learning algorithms can identify problems before they happen. Sometimes the term machine learning is used interchangeably with artificial intelligence. Originating in 1956, artificial intelligence refers generally to the ability of computers to perform tasks requiring human intervention. By the 1980s, organizations had the computing resources to analyze large volumes of data and make predictions based on the results. Machine learning gained popularity.
You might also hear the term deep learning or neural network. Introduced in 2012, deep learning is the ability of computers to arrive at conclusions based on data representations and media, images, and video, rather than using algorithms. A system that is capable of deep learning uses a neural network that is modeled on the human brain. Machine learning consists of interconnected groups of nodes organized into layers and can perform complex tasks like recognizing patterns.
Machine learning has two main types of applications, classification, and prediction.
Classification involves grouping or categorizing raw data.
Prediction is finding patterns in data and using them to forecast. Consider how Facebook identifies people in photos. Using machine learning, the Facebook algorithm combs through photos and matches user's faces. This is an example of classification.
Conversely, Netflix accumulates data about all the movies a customer watches, and then through its recommendation engine, suggests new movies. That's an example of prediction. For these companies, machine learning boosts service efficiency and improves customers experiences. It helps organizations generate revenue and reduce expenses.
To stay competitive, organizations must keep up with and capitalize on technological advances. This includes considering how machine learning could make them more efficient, adaptable, and responsive to customers' needs.
Benefits of Machine Learning
Potential applications of machine learning are still being discovered and explored. But already, machine learning is providing enormous benefits for businesses. For instance, The most magical place on earth boasts its own machine learning system.
Disney World has an entire network devoted to using machine learning, to identify and satisfy customer needs. All visitors to Disney World have the option to wear Magic Bands which come equipped with a radio frequency identification tag and long range radio. The bands send information to a central system. And for park visitors, the bands act as keys, credit cards, tickets and fast passes to all attractions. When customer swipe their bands, the system knows where they are. What they're up to any given second, and what they might need.
Disney World System results in less wait time for customers. When visitors are inconvenienced, the system reroutes them or even provides coupons for the park. This translates into better efficiency and customer service. And ultimately, better revenue and more profit. Less wait time means more fun. And more fun means more repeat visits, and more recommendations to friends and family.
- A key benefit of machine learning is that it can help organizations gain new business insights. For example, some companies use machine learning to predict when to release products, or when to end production. Others are using it to scan resumes to find the best job candidates or to make investment decisions, manage their inventories, or predict customer behavior.
- A second key benefit is that it can help organizations gain business insights faster than ever before, often in real-time. This means businesses can also respond faster to insights.
- A third key benefit is that machine learning can free up staff to do more valuable work. Employees provide more value if they're not caught up in repetitive and routine tasks, which require few decision-making skills.
Identifying Opportunities for Machine Learning
Artificial intelligence and machine learning applications get a lot of attention because they have great potential for businesses. However, no amount of technology is valuable unless it supports your business needs. To benefit, you must identify the best opportunities for using machine learning in your organization. To recognize those opportunities requires following a set of steps.
The first step is to catalog the organization's business processes and establish which processes make good candidates for machine learning. Straightforward and repeatable processes, like calculating employees' monthly pay, only require simple automation processes, not machine learning. Suitable candidates for machine learning include cases where volumes of complex data need to be interpreted. These are processes that require prediction or classification.
The second step is identifying when to use machine learning. It's important to distinguish between automation, which relies on simple business logic, and machine learning, which is more complex and involves adapting and reaching conclusions based on the data that enters a system, not all processes are improved using machine learning.
Third, start with simple problems. A suitable entry point is identifying questions yielding yes or no answers. Or categorical answers that can be slotted into a table format. Data in a tabulated form is easier for a system to read and interpret. Developers can follow up with more questions once the model has a solid foundation.
Next, identify problems with suitable attributes for machine learning. Processes involving classification and prediction, and performed regularly by many individuals, are suitable candidates because machine learning can make those kinds of jobs easier. Perhaps ten people arrive at the same conclusion after performing a task. Afterward, a second round of questioning produces a different result. Maybe one person didn't reach the same conclusion. Subsequent conclusions could be different, which machine learning will learn to pick up on.
Machine learning uncovers hidden insights through learning from historical relationships and trends in the data. If a process used by many employees can be augmented with machine learning, it will make their jobs easier.
Lastly, a data team must identify or set up the appropriate data sources. Company records provide a suitable foundation. Depending on the problem, an existing customer or financial database may provide valuable data for training a machine learning application. Sitting down with a data science team and outlining the thought process behind it, will be useful for developing the machine learning model.
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