Applied machine learning refers to the application of machine learning (ML) to address different data-related problems. Its connotation is similar to applied mathematics—pure math involves many theories, which are applied and put to practical use in applied mathematics. As a result, applied mathematics helps solve real-world problems in engineering, biology, business, and many other fields.
Similarly, applied machine learning takes one’s understanding of ML concepts and theories and uses these to solve problems. For example, a fundamental concept in ML is supervised learning, an ML task that requires labeled data and follows a path toward obtaining the desired output. The concept is pretty straightforward, but how can this address real-world problems?
This ML concept can be applied in computing credit scores and automating credit approval. You feed the machine with credit-related data, such as credit history and limit utilization (i.e., labeled data), and teach it to compute the credit score of a particular person (i.e., desired output). If the credit score reaches the minimum acceptable level, the credit application is approved.
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ML’s origin can be traced back to 1952, when Arthur Samuel created the first ML program. The goal of the program seemed simple—to play a game of checkers. But the underlying application of ML was at work. The program was taught to correct its past mistakes and get better at playing checkers.
Sure, the start of ML didn’t solve real problems instantaneously, but it set the stage for what is applied machine learning today. These days, you can see applications of ML every day. Aside from the credit score example above, below are a few more applied machine learning examples.
Applied Machine Learning Examples
When you ask Siri to open an application on your phone or search for something on the Internet, you are witnessing applied machine learning at work. The program, which already contains data in the form of text, translates spoken words into text. Siri also learns these words to provide better experiences for users continuously.
When you view a product on Amazon, what else do you see? Chances are that at the bottom of the product description, you would see the “Customers also bought these items” section. For example, when viewing a Fire Tablet listing, Amazon pitches in a kid-proof case, micro-USB cable, and charger, along with different variations of the tablet.
This is applied machine learning in action, which is specifically called “learning association.” Machines are taught to associate one object with another.
By the way, the same concept can be applied to detecting malicious activities within a network. Your anti-malware warns you about the file you’re about to download because it associates the file with another object that is malicious.
In ML, pattern recognition refers to the process of putting a label on specific data based on regularities. For instance, when you keep watching sci-fi movies, Netflix would detect that pattern and recommend movies under the same genre.
Pattern recognition can be applied in other fields, such as detecting signs of cancer in mammograms.
The Basic Process Involved in Applied Machine Learning
We now know the answer to the question “What is applied machine learning?” It is the application of ML to solve real-world problems. As such, the first step in the process is to define the problem you want to solve.
In the related products example above, the problem Amazon tried to solve could be how to increase merchants’ sales. By recommending products related to the items the user is searching for, merchants are given more visibility and a better chance of making a sale.
The next step in applied machine learning is gathering training data. In some cases, the data is already on hand. When applying ML to compute credit scores, the financial institution already has the data it needs. It just has to relay the information safely to the developers.
While there are several technical steps in between, such as writing the code and programming the algorithm, another essential step is maintaining the ML models.
Applied machine learning does not have a final product that you can wrap and ship to customers. Instead, businesses purchase solutions to their problems. These solutions continue to evolve and expand as machines learn more. There may also come a time when a company’s problem changes, and the current machine won’t fit the bill anymore.