Machine Learning – What Is It and How Can I Use It?

In recent years, there has been a lot of talk about machine learning in the digital world. But what exactly is machine learning? How does it work? And what are its applications? Well, let’s figure that out now.

Machine learning is a field within AI (Artificial Intelligence) that focuses on enabling computers to independently understand and process large amounts of data.
In the past, we told computers what to do and how to use them through traditional programming. With machine learning, we use different types of algorithms in computers so that they become smarter and faster over time. The more data and information a computer processes, the smarter it becomes. It learns to interpret and process large amounts of data so that it can then predict patterns—patterns that are too complex for a human to interpret.

How can a computer learn things the way a human does?

Yes, the answer to that question is quite simple. Computers learn new things in the same way we humans do.
When we receive a stimulus—for example, through an image—that information is sent to our brain. And if it’s something we’ve seen before, there are likely already strong connections that allow us to understand and recognize what we see. In other words, computers must be trained to perform a specific task—and they work a bit like children—they learn by thinking for themselves and experimenting.

Machine learning consists of three distinct phases: learning, training, and evaluation.

1. Learning

The first step in machine learning is to teach a computer to perform a specific task. To do this, you can use two different methods: supervised and unsupervised learning.

Supervised learning is the most common form of machine learning. It involves training the computer using labeled data—that is, data that contains examples of the desired responses. This method is used, for example, to identify counterfeit credit cards; in such cases, a dataset containing both known fraudulent and valid transactions is used to train the computer.
Unsupervised learning involves training the computer using unlabeled data. The goal is to teach the computer to identify relationships. This method is used, for example, to teach the computer to identify and group customers with similar purchasing habits.

2. Exercise

The next step in machine learning is to start training your computer to produce the most accurate results possible. So, if you’ve decided to use supervised learning to identify counterfeit credit cards, as in the example above, you’ll start by feeding the computer data that includes both correct and incorrect information.

Next, you randomly split the data, using one part to train the computer and another part to test or evaluate the computer’s ability to predict events.

3. Evaluation

Once you have a trained model, you need to evaluate it using the remaining test data. In other words, you use data with a result you already know to see if your model makes accurate predictions. It’s important to remember that machine learning is an ongoing process and that your model gets smarter and faster the more data you add. There are also different types of algorithms suited for different types of expected outcomes.

Why should I use machine learning?

Do you want to easily draw conclusions from large amounts of customer data? Or quickly tailor your communication or offers based on newly discovered information? If so, you should invest in machine learning. You can apply machine learning throughout the entire customer lifecycle. It will help you identify patterns, such as when a customer needs to expand their business or is about to leave you.
You can also use Machine Learning to analyze historical data and make intelligent predictions about the future—for example, by forecasting what will happen—which will help you plan and allocate your resources more effectively.

Three tips for anyone considering implementing machine learning

1. Make sure you have plenty of data to work with. This will help you obtain accurate data and analysis more quickly.
   Be patient. Machine learning rarely gets it right the first time.
2. Your computer needs to train in peace and quiet in order to predict behaviors and events. Again: The more data you have, the better.
3. Experiment. Don’t be afraid to analyze the results and try adding or removing parameters. Think about what you can do better to get the most accurate data possible.

Machine learning helps you identify patterns that the human eye or brain cannot detect, which is why it has become a very popular and important tool—especially in the digital landscape.

Would you like to learn more about how we at Releye can help you with machine learning? Contact us!