Artificial Intelligence (AI) refers to the ability of computers to think like humans and perform similar tasks as people do. While computer scientists have yet to create a near-perfect AI that genuinely feels as a human being does, there are already AI applications that help improve day-to-day activities. You may take AI virtual assistants as a prime example. These applications help you do various tasks such as setting an appointment in your calendar, set an alarm, and more. These types of programs have helped individuals and businesses achieve better efficiency and productivity.

If you’ve heard of AI, you may have also heard about ML or machine learning. It’s a subtopic of AI focused on helping computers learn the same way humans do. People primarily develop machine learning applications by creating models that will guide how the computer thinks. ML models are based on labeled or unlabelled data that the computer scientist or software engineer feeds.

If you want to further learn about machine learning, then you must understand the two main types of machine learning—supervised and unsupervised machine learning. Supervised machine learning essentially involves more human intervention, unlike unsupervised machine learning.

In this article, you’ll learn more about unsupervised machine learning.

• Algorithms

Unsupervised machine learning involves three main tasks: clustering, dimensionality reduction, and association rules. Within these tasks, specific algorithms are used according to the needs of the job or project. That said, here are brief explanations of each main task used in unsupervised machine learning:

• Clustering

Clustering is a technique used for data mining wherein unlabelled data are grouped according to their similarities or differences. To learn more about this method, you may read this guide to clustering algorithms. That said, below are some of the algorithms used to perform this task:

• K-Means Clustering

This is an instance of exclusive clustering and is often used in market segmentation and image compression. It first identifies the ‘k’ number of centroids. A centroid refers to the imaginary or actual location of the center of a cluster. From there, data points are grouped according to the closest centroid.

• Hierarchal Clustering

There’re two kinds of this algorithm: agglomerative and divisive, with the former initially isolating data points into separate groups. Then they are merged iteratively based on similarity until a cluster is achieved. Meanwhile, it’s the opposite for divisiveness, with clusters being made out of differences.

• Probabilistic Clustering

Data points are clustered based on how likely they are to be a part of a particular distribution. The Gaussian Mixture Model (GMM) is the most commonly used method.

• Dimensionality Reduction

Dimensionality reduction involves reducing the number of dimensions or features in a given dataset in the event that it’s too large. For this, the following algorithms are used:

• Principal Component Analysis (PCA)

This algorithm is used to lessen the redundancies in a dataset with a large volume using feature extraction.

• Single Value Decomposition (SVD)

On the other hand, SVD factorizes a matrix denoted with the letter ‘A’ into three lower-ranking matrices.

• Autoencoders

Autoencoders take advantage of neural networks and find a way to create a new representation for the original data’s input. In doing so, this becomes useful for compressing data.

• Association Rules

Association rules focus on finding the relationships between the variables within a given dataset. This has been found to be very helpful in marketing to understand customers’ consumption habits. However, it can be used for recommendation systems too.

For this task, the apriori algorithm is used. Online retailers use this to personalize the shopping experience of consumers online. Aside from e-commerce stores, streaming platforms also use this technology to recommend media content that the user would probably like.

This algorithm is used in transactional datasets. From that, platforms try to determine the likelihood that a user will buy a particular item based on their consumption or purchase of another product.

• Applications

Having established the difference between supervised and unsupervised machine learning, you may want to learn about the applications of unsupervised machine learning. Knowing some applications may help you decide if unsupervised machine learning is for your project.

Unsupervised machine learning is often used for projects involving large volumes of unlabeled data that help companies and developers gain new insight from the dataset. They can be used in the following:

• Recommendation systems
• Customer segmentation
• Anomaly detection
• Segmentation of products
• Preparing data for supervised machine learning as you can use unsupervised to label the data for you

Advantages and Disadvantages

Like any other method, unsupervised machine learning has its share of advantages and disadvantages.

Advantages

• Helps with handling large volumes of data
• No expertise is required initially
• It may be considered time-efficient as you don’t need to spend time labelilng the data.

Disadvantages

• Outliers may cause results to vary
• High risk for computer mislabeling data
• You’ll need an expert on the subject to interpret the results 

Concluding thoughts…

AI and ML are becoming prominent fields of study in computers and data analysis for businesses and consumers. To better understand how ML works, it is best to study its two main types first—supervised and unsupervised machine learning.

This guide has dwelled on the basics of unsupervised machine learning. As you can probably tell, you can encounter unsupervised machine learning whenever you’re shopping online or looking for new shows and movies to watch.

The retail industry has witnessed tremendous growth in recent years owing to its ability to use technology to its advantage.

Following the e-commerce trends, for example, many retail store owners were not only able to meet their customer’s demands during the pandemic but also managed to take customers’ shopping experience to a whole new level.

By making the most of the digital space, retail store owners have given themselves the upper hand when it comes to attracting, retaining, and satisfying their customers.

If you are looking forward to take your retail store operations a notch further, then your best bet is to keep track of the latest innovations and introduce them to your store.

Here we have listed some of the latest tech trends that are being followed by the retail industry:

• Using E-commerce-friendly Point of Sale (POS) systems

E-commerce was literally the only thing that kept the retail industry going during the pandemic. By enabling the retailers to deliver customers’ orders right to their doorstep, the online sales process boomed.

That’s exactly why many retailers felt the need to optimise their e-commerce platforms in order to keep up with the competition.

But that isn’t enough.

According to industry professionals, it is important for retailers to bring in fully integrated POS and e-commerce solutions to stay ahead in the game. The system is actually a combination of both hardware and software that ends up facilitating your regular business operations.

All in all, it tracks and organises your store’s information and streamlines the entire process.

• Introducing Digital Shelf Labels

Gone are the days when retail store owners used to have product labels printed exclusively for each unique item in their store. With the help of electronic shelf labels, retailers can actually get rid of the hassle of attaching those traditional labels in their stores or inventory.

Now, what are electronic shelf labels?

ESL or electronic shelf label system is actually used by retailers to display the pricing of their products on shelves. The best part of using this system is that the pricing of the product updates automatically whenever it fluctuates.

In fact, according to the professionals at SES-imagotag.com, smart and electronic price tags are typically becoming the portal for cloud-based applications that are responsible for focusing on store automation and data analytics.

Not only does it track the customer behaviour, but it also makes things a lot easier for your employees.

• Upgrading to contact-less stores

Most customers still prefer contact-less transactions and self-checkout options in retail stores.

That’s why it would be a great idea to introduce technical vision systems, IoT devices, facial recognition technology, and machine learning into your retail stores.

This way, your systems can easily keep track of all the items your consumers place into their carts and then automatically charge their credit cards during the checkout process.

You can also use barcode scanners or QR codes in your retail stores so that your customers can pay their bills with their smartphones right away.

Concluding thoughts…

The retail industry has been evolving over time, and tools like machine learning, augmented reality, along with data science are definitely streamlining the process.

By embracing the tech trends mentioned retailers are witnessing significant growth in their businesses.