Unsupervised learning is the use of AI algorithms for identifying patterns in datasets that have neither labeled nor classified data points. The algorithms are used for classifying, labeling, and grouping the data points in the dataset without any external guidance. In simple terms, unsupervised learning helps the system in identifying patterns in the datasets all on its own. 

Unsupervised learning includes an AI system grouping unsorted information as per the similarities and differences, even though no categories are provided. It can be used for performing more complex processing tasks than the supervised learning systems. Also, one of the ways of testing AI is subjecting a system through unsupervised learning. 

Unsupervised learning begins when Data Scientists or Machine Learning engineers pass datasets through algorithms for training them. As mentioned above, these datasets don’t have any categories or labels that can be used for training the systems. Every single piece of data passed through the algorithms for training is unlabeled. 

The objective of unsupervised learning is to allow the algorithms to identify trends and patterns in the training datasets and then group or categorize the input objects on the basis of the identified patterns. The algorithm extracts the useful features or information from the datasets by analyzing the underlying structure. Algorithms use the unstructured inputs for developing specific outputs. This is done by analyzing the relationship between each input object or sample.

Take the example of animal datasets containing their images. Algorithms will be used for classifying the animals into groups like those with scales, those with feathers, and those with fur. Then, the images may be grouped in more specific subgroups for learning distinctions in each category.

Algorithms uncover and identify patterns to do this categorization. In unsupervised learning, pattern recognition is done without feeding data into the system that teaches it how to distinguish (In this example, between fishes, mammals, and birds, and further distinguishing the mammals’ category between cats and dogs).

The most basic difference between unsupervised and supervised learning is that supervised learning involves using labeled datasets to train algorithms for identifying and sorting data based on provided labels. The sample or input object will have a corresponding label so that algorithms can learn to identify and classifying input objects that match with the label.

Basically, algorithms are creating maps from inputs to specific outputs on the basis of what they learned from training data. This data is labeled by Data Scientists or Machine Learning Engineers. Also, in supervised learning, labeled training data as well as labeled validation data is used. This allows the supervised learning outputs’ accuracy to be checked. You cannot measure unsupervised learning in this way. Data Scientists or Machine Learning Engineers can choose to use a mix of labeled and unlabeled data for training their algorithms. This is an in-between option known as semi-supervised learning. 

Unsupervised learning is usually focused on clustering algorithms. In simple terms, clustering is the process of grouping data points or objects that are similar and dissimilar to other objects in other clusters. Data Scientists and Machine Learning Engineers use different algorithms to cluster objects together. These algorithms fall into the following different categories on the basis of how they work:

● Exclusive clustering

● Hierarchical clustering

● Overlapping clustering

● Probabilistic clustering

Some of the most commonly used algorithms are k-means clustering algorithms, fuzzy k-means algorithms, density-based clustering algorithms, and hierarchical clustering algorithms. The Gaussian mixture models and the Latent Dirichlet Allocation (LDA) model are also used in clustering. Apart from clustering, you can use unsupervised learning for determining the density estimation of data or how the data is distributed in the space.

Dimensionality Reduction and Exploratory Analysis are some of the most common uses of unsupervised learning.

In Dimensionality reduction, algorithms are used for reducing the number of features, variables, or dimensions in the datasets so that the focus is given to relevant features for different objectives. You can also say that dimensionality reduction is a way of removing noisy data. Machine Learning Engineers also use latent variables, model-based algorithms for doing this work. For example, an organization can read blurry images by reducing the background using dimensionality reduction.

Exploratory Analysis involves using algorithms for detecting patterns that weren’t known before. It has a wide range of industry applications. A common example of this is businesses using the exploratory analysis to start their customer segmentation efforts.

● Association Mining - This involves using algorithms for finding associations between the data points. This is often used by retailers for identifying the products that are often bought together.

● Clustering Anomaly Detection - In this, algorithms are used for identifying any unusual data points present in the datasets. This capability is specifically useful for identifying human errors, faulty products, or fraudulent activities.

Even though unsupervised Learning offers several features to the organizations, there are a few disadvantages as well, including the following:

● The accuracy of the outputs of Unsupervised Learning is uncertain.

● Checking how accurate the outputs of Unsupervised Learning is difficult because of the absence of unlabeled data sets for verifying the results.

● With Unsupervised Learning, Data Scientists, and Machine Learning Engineers have to spend more time labeling and interpreting results than they would spend with Supervised Learning.

● There is a lack of complete insight into why or how an unsupervised system gets the results.

Another added disadvantage of Unsupervised Learning is associated with clustering. During cluster analysis, the similarities between the input objects can be overestimated. This can obscure a few individual data points that might be crucial for some use cases. For example, in customer segmentation where the objective is understanding individual customers and their buying habits.

However, even with all these disadvantages, Unsupervised Learning is a popular technique for Machine Learning. It can help in identifying patterns in data that were previously unknown. Also, it is faster, easier, and cheaper than Supervised Learning. This is because unlike the Supervised Learning, there is no manual work of labeling data associated with Unsupervised Learning. If you want to learn more about Unsupervised Learning, you can enroll in a Machine Learning online course that will help you learn how to identify patterns in real-time data.