Unsupervised Machine Learning: What it is & How to Use it

Sometimes in machine learning you will come across unlabeled datasets. What this means is that the dataset has an input feature, but no output feature, which can be a problem for supervised training techniques. When it comes to unsupervised machine learning, this is not a problem. 

Unsupervised machine learning doesn’t need any data labeling, and it is able to glean valuable information and insights from the data that it is presented with, even though it has no output variables to train with. This method of machine learning is very good at finding hidden correlations and patterns in datasets which gives it the ability to operate even with unknown outcomes, generating new and novel results.

In this article we will look at some common topics in unsupervised machine learning such as dimensionality, clustering, and anomaly detection, among other things. Let’s dive in and see what we can discover about this fascinating approach to Unsupervised Machine Learning! Check here for some of the reasons that you should be getting your AI-900 exam certification.

Understanding Unsupervised Machine Learning: What Is It?

The simplified explanation of what unsupervised machine learning is, is that it is a method of discovering patterns and hidden structures within data without the need for labeling. Human beings are great at discovering patterns in nature and in our environment, but unsupervised machine learning takes this ability to a whole new level.

It is able to parse large sections of data that would make little sense to us, and it can find useful connections between seemingly unrelated data points. The end result is a model that can deal with unexpected events in a data set and offer us answers that we might not have ever thought about. These types of models are versatile, and we see them being used in scientific and research, all the way to sales and marketing analytics. Strange data and hidden patterns are everywhere.

Next, we will look at some basic details about techniques that are commonly used in unsupervised machine learning such as dimensionality reduction, anomaly detection, and clustering. 

Unsupervised Machine Learning Techniques: Clustering, Anomaly Detection, and Dimensionality Reduction.

There needs to be some effective techniques and methods in order to successfully glean useful information out of unlabeled and seemingly unrelated datasets. The three most common ones that we want to look at are:

Clustering: This is a technique that you have probably heard many times before. It groups data points that have features that are similar to one another, and then finds any useful relationships between them. It also finds the data structures that we mentioned earlier, making the data easier for subsequent training methods to make sense of the data.

Dimensionality Reduction: This method does exactly what it says in its name – it reduces the dimensions within the data. This means that it simplifies the features that are nested within the dataset and makes it easier to parse. It reduces the noise within the data and improves the efficiency of other machine learning algorithms that might be applied later. 

Anomaly Detection: Anomaly detection allows trainers to pick up on events of interest within the dataset. If there has been fraud, a credit issue, or any other rare event then it will be flagged in the case of a financial dataset. Other fields will keep a close eye on the parameters that they have set as part of their training.

These essential techniques make unsupervised machine learning possible and are detailed and advanced enough for us to make entire articles for each of them, but for our purposes we are simply scratching the surface to get a basic understanding of what happens when training an unsupervised model.

Challenges and Difficulties in Unsupervised Learning

As with most things in machine learning, there are choices that need to be made when you are deciding which approach to take with your project. Below are some of the most common issues that you will face when using unsupervised machine learning.

Ground Truth: There is a common misconception that just because there is a lack of ground truth in these types of datasets and techniques, that there is nothing there. In fact, a lack of ground truth is the absence of an ideal expected result, and not a lack of true data. Clustering relies on the researcher having an understanding of the data that is being worked with, or access to data sources that will help with determining what the ideal expected results should be. 

Algorithm Choices: Every dataset will require a different approach depending on what the desired outcomes are. Experimentation is usually the best approach, and researchers will often try out different algorithms to find the best result that matches the intent of the training objectives.

Choosing the Right Parameters: Parameter Selection usually introduces performance constraints, as unsupervised learning techniques often require manual parameter selection. Researchers must again experiment to find the optimal parameter count to get the best results.

Wrapping Up

Trying to make sense of mountains of data is not something that we do very well as humans. We like to find meaning and patterns in things, but when it comes to walls of plaintext, or pixelated images that make no sense to us, then it is time to call in the machines. Azure has some excellent machine learning services that you can learn more about.

Now that we know what the potential limitations of this training method are, we can look at ways to improve our workflows, and incorporate best practices into our own machine learning projects. If you want to get started with machine learning, look no further than CBT Nuggets Machine Learning & AI-900 course. It will give you all the fundamentals that you need to get started in artificial intelligence and machine learning.