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Unless you’ve been living under a rock, you’ve probably heard about machine learning at some point. So, what are machine learning methods? This powerful data science tool has helped analysts, data scientists, and business owners make accurate predictions, sort data points, and find patterns in information.
While that overview may sound impressive in itself, it only offers a glimpse into what machine learning does for its human counterparts.
Curious to learn more about how ML works? In this article, we’ll walk you through everything there is to know about machine learning.
Machine learning (ML) is the marriage of computer science and artificial intelligence. It is a study performed using computer algorithms that continuously improve over time. It can be applied to a variety of fields, from cybersecurity to voice recognition.
How Does Machine Learning Work?
If you want to understand how ML works, psychology is a great place to look. Abraham Kaplan once said, "Give a small boy a hammer, and he will find that everything he encounters needs pounding." That statement perfectly demonstrates the driving force behind machine learning.
When a computer is set up to learn something, its algorithms will test several variables repeatedly until the computer finds a solution that works. The more variables it tests, the more data it can compare with its results. The more often it compares its results, the faster machine learning will find its solution. It's a simple but effective process that happens all the time in real life.
Machine Learning Methods
What are the 3 Types of Machine Learning?
Machine learning methods can be classified into three primary categories: unsupervised, supervised, and semi-supervised.
With unsupervised learning, the computer draws conclusions from the data it collects without any outside source telling it what those conclusions should be.
This type of learning is beneficial for analysis purposes, but its development is limited because there's nothing to compare it with. Furthermore, since you're feeding unlabeled, input-only data, there is no real way to measure how accurate the results are.
Unsupervised learning has its place in machine learning, but it's generally used for exploratory analysis or to generate the data that later becomes the input for other analyses.
For example, if you were looking for patterns in consumer spending habits, you could perform unsupervised learning on the raw purchase data. Doing so would allow you to uncover trends and predict what other spending habits your consumers might have.
Supervised learning, on the other hand, offers a human-instructed analysis. The human tells the computer which data points are relevant and which ones are not. Suppose you were looking for patterns in consumer spending habits. In that case, you could perform supervised learning on the raw purchase data so that the computer could tell you that, for example, people who bought peanut butter also tend to buy jelly.
Supervised learning is more likely to produce accurate results than unsupervised learning because it relies on human input rather than just observation. You can even use it to make predictions and take corrective actions (if necessary.)
For example, if a company has a dormant customer, they could run supervised learning on data to predict how likely it is for this customer to stop making purchases. This might help the company identify users in danger of leaving, thereby giving them time to implement strategies to retain them.
When unsupervised and supervised learning come together, you get semi-supervised learning. Like supervised learning, it uses a human-instructed analysis. However, it also allows the computer to draw conclusions based on its observations.
Unsupervised learning is the passive collection of input data. In contrast, semi-supervised learning is a mix of both input and observed data. This type of machine learning uses a process called clustering to separate relevant from irrelevant data.
For example, you could use unsupervised learning to look for patterns in email data. Then, you could use supervised learning to determine which of these patterns are relevant and which aren't. Then, you can use semi-supervised learning to cluster the relevant patterns together and examine them one at a time.
This kind of machine learning is extremely powerful because it allows multiple types of data to be examined simultaneously and holistically.
Predictive analytics uses machine learning to develop algorithms that can predict outcomes using real-time data. For example, salespeople can use predictive analytics to predict which customers are about to churn.
Machine learning has become a popular tool in cybersecurity. Why? Simple.
Cybercriminals are constantly changing patterns and attack methods, and since machine learning can be used to detect unusual patterns in digital security, it helps identify and prevent cyber threats, making it a valuable addition to your organization's cybersecurity arsenal alongside robust antivirus solutions and secure VPNs.
Machine learning can be used to improve voice recognition. By analyzing human speech patterns, machine algorithms can understand what certain words and phrases sound like.
For example, a lot of people mispronounce "supercalifragilisticexpialidocious." When you ask Alexa how you're supposed to say the full title of Mary Poppins, it will know that even though you're saying the shortened version of the word, that's not how it's supposed to sound.
We’re certain you’re familiar with Alexa, Siri, Google Assistant. But did you know that all these virtual assistants are backed by machine learning? So every time you give an instruction—make a call, play a soundtrack, access your email, etc.—your virtual assistant is employing machine learning to understand and act.
Medical Predictions and Diagnosis
Machine learning is helping researchers develop artificial intelligence capable of predicting medical events before they happen. This could be used to predict strokes and heart attacks, for example.
But it will also be able to predict which people are likely to get sick in the first place, which will help doctors better understand who might need something like preventative medication.
Machine learning is changing how we conduct business, keep tabs on our kids, and even make home-improvement decisions. It powers your Netflix feed, your CRM software, even self-drive cars. If you’re now wondering how to leverage machine learning for your business, head on to Graphite and check out available no-code machine learning models.
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This blog post provides insights based on the current research and understanding of AI, machine learning and predictive analytics applications for companies. Businesses should use this information as a guide and seek professional advice when developing and implementing new strategies.
At Graphite Note, we are committed to providing our readers with accurate and up-to-date information. Our content is regularly reviewed and updated to reflect the latest advancements in the field of predictive analytics and AI.
Hrvoje Smolic, is the accomplished Founder and CEO of Graphite Note. He holds a Master's degree in Physics from the University of Zagreb. In 2010 Hrvoje founded Qualia, a company that created BusinessQ, an innovative SaaS data visualization software utilized by over 15,000 companies worldwide. Continuing his entrepreneurial journey, Hrvoje founded Graphite Note in 2020, a visionary company that seeks to redefine the business intelligence landscape by seamlessly integrating data analytics, predictive analytics algorithms, and effective human communication.
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