Are you just now stumbling onto the term machine learning: unsupervised learning?
This fascinating technology leaves an Machine Learning algorithms to learn on their own using plain, unlabeled examples - often revealing interesting insights in the process.
Typically used by analysts to find hidden patterns in data sets, its beauty lies in the fact that it needs no human intervention. That means you won't need to sit down and assist in its processes.
But how exactly does unsupervised learning benefit you or impact your decisions? Dive into the world of unsupervised machine learning to find out more.
Also known as unsupervised learning, it uses machine learning to categorize and analyze unlabeled data. Often used in exploratory data analysis, its wide range of applications endear it to many business owners and strategists.
Unlike supervised machine learning, you're working blindly without clear values. As such, it can't be applied to most machine learning approaches that require specific data values - or labeled datasets.
However, while it can't be applied directly to problems that require regression and classification results, it's quite helpful in helping you discover the structure of your data. This makes it useful across a wide range of other data science applications - ranging from customer analytics to understanding whale language.
Unsupervised machine learning models use input data only to learn. It then applies specific algorithms to automatically analyze the data sets. After that, the data is segmented into groups.
Its main goal is to figure out relationships within the dataset it's fed. As such, it's more frequently used to gather results when you don't necessarily know what to expect.
Put simply, unsupervised learning uses input data to find the value of output data.
Unsupervised machine learning is more commonly used to help you understand your existing customer base on a deeper level. Since there is no way to measure the accuracy of its results, unsupervised machine learning shouldn't be used to analyze data where you have an expected output.
Despite its shortcomings, unsupervised machine learning is still a powerful data analysis tool that can help you find unknown patterns. Because of this, it's also often used in cybersecurity to help determine hacking patterns.
Unsupervised learning models are often used to accomplish three main tasks. Depending on your needs, it's important to know which approach might work for you. Take a look at these approaches below.
The most common approach, clustering groups input-only data based on similarities and differences. This is helpful for finding specific patterns in the information you provide the model with - such as customer activity.
There are currently four sub-approaches when it comes to clustering.
This method follows a specific set of rules to determine relationships between data points. Usually, it’s used in market basket analytics where businesses analyze customer activity based on specific patterns.
You’ll see these most often applied in cross-selling and upselling tactics or recommendation engines like those you see on marketplaces such as Amazon. If you’ve ever seen a “What’s trending today” section on your browser - this is most likely the fruit of the association rules approach.
This approach is used to prevent overfitting. When your data set has a high value density, dimensionality reduction minimizes the data inputs into smaller, bite-sized pieces. What’s even more impressive is that it does so without harming the integrity of your data.
There are several dimensionality reduction methods used to preprocess data such as:
No-code machine learning for everyday business users.
Unsupervised learning is mainly used to help improve user or customer experience. Beyond that, it also has applications in cybersecurity, social networking, and quality assurance for systems.
In its purest sense, UML can give you a glimpse of large data sets to help you cultivate a result. Take a look at its most common applications below:
Marketplaces and webstores often apply “customer who bought this also bought” techniques. This not only drives interest in certain products, but it also helps upsell customers in a way that looks like you’re providing added value to their experience.
Google News is one of the best examples when it comes to unsupervised learning. The platform categorizes its articles into sections labeled under specific themes to make it easier for their readers to find relevant information.
Object recognition is one of the most common examples of applied unsupervised machine learning. These perception tasks help computers index information for recognizing objects like when you want your camera to autofocus on a subject.
While unsupervised learning can be used to find similarities in a data set, it’s also efficient in finding new activity. Commonly applied in cybersecurity, anomalies alert the analyst when there is a potential threat in security, or when questionable activities happen within your servers.
Unsupervised machine learning may not be able to provide you with specific values, but it’s highly effective when it comes to giving you answers. From identifying your customer activities to creating pitch-perfect recommendations, this machine learning method shouldn’t be ignored.
If you want to leverage the full potential of unsupervised machine learning, then it’s a good idea to get in touch with industry experts as soon as possible.