Coursera Learner working on a presentation with Coursera logo and
Coursera Learner working on a presentation with Coursera logo and

When a model learns patterns and shares the information, it requires accurate data to help the machine learn those patterns. This is what machine learning is all about. With various techniques and methods, you train your machine so it can perform tasks using artificial intelligence. This technique is a popular form of Machine learning, but there are various flaws.

We face various challenges in training models using this method. For starters, labeling data will bother you the most; you cannot find accurately labeled data to feed the model. Moreover, the data costs a lot, and sometimes, it does not work as you want. Another technique is in the works and is yet to gain mainstream popularity, so you can expect to see this technology in future advancement.

The technique is unsupervised learning. This learning technique does not include data with labels or patterns. Instead, you provide the model with limited raw data. The algorithm of the machine will process the data, and the outcome will be a new pattern and labels. In this article, we will learn about unsupervised learning in-depth.

What is Unsupervised Learning

In this method or technique, you do not have to supervise or share labeled data with the model. Instead, the algorithm of the model will automatically understand and start learning from the data without guidance. The model will use the unlabeled data to identify new patterns and information due to the design of their algorithm. With this method, we can find new and previously unidentified information.

This type of learning behavior is similar to that of humans’. Imagine how we analyze and observe the surroundings to gather the data and understand and recognize things. Similarly, machines with unsupervised learning algorithm uncover patterns to find useful results. For instance, the system can identify the difference between cats and dogs by understanding both animals’ features and characteristics.

How the Algorithm of Unsupervised Learning Works

Unsupervised algorithms work without any proper training. It works as soon as it receives the data. The algorithm makes its own decisions and finds ways to sort variables and check if they fit together. Another benefit of this method is that you do not have to provide labeled data. The system will explore the data and define rules accordingly. There is a definite process of working for the output in an unsupervised learning algorithm. Here are some of the steps in which this algorithm works:

• This algorithm will explore the data structure and define its own pattern.
• Extract useful insights that can be used for analyzing the output.
• It helps in making the decision-making process even productive.
In simple words, this algorithm describes the information and identifies the categories so that you can easily understand data from insights. There are two major techniques to apply unsupervised learning technique

• Clustering
• Dimensionality Reduction

Unsupervised Neural Networks

These neural networks are trained on the labeled data so they can identify the regression and classification. This machine learning is supervised machine learning. These neural networks are also trained directly on unlabeled data through unsupervised schemes.

Techniques of Unsupervised Learning

1. Clustering

Clustering is one of the important and popular algorithm techniques for unsupervised learning. This algorithm finds the pattern and categorizes the collection of the data. In this method, you can process the data and identify the groups from that data. In this type of unsupervised learning, you can also define how many groups you want to find. Clustering further divides into different groups:

• Exclusive
In this data grouping method, you can only sort the data so that a single data can only belong to a cluster. The example of this method include K-means

• Agglomerative
In an agglomerative algorithm, every data is a cluster. The relationship between the two clusters will diminish the number of clusters in the output. An example of this unsupervised learning is Hierarchical clustering.

• Overlapping
The overlapping algorithm will include each data into multiple cluster data. This means that each data will include in more than one cluster, depending on the membership values—for example, Fuzzy C-Means.

• Probabilistic
In this method, the data distributes in the cluster based on the factors they include. For instance, in man’s shoes, women’s shoes, man’s gloves, women’s gloves, the algorithm will make two clusters, gloves, and shoes.

2. Dimensionality Reduction

The machine learning classification and problems are solved through these methods depending on many factors. These factors are called features and are the variables of the data. The more features you provide the algorithm, the harder it becomes to understand the training sets. These features are sometimes redundant and correlated. That is when you need help from the dimensionality reduction algorithm. This unsupervised algorithm will reduce the random variables and obtain a principle for these variables. The algorithm divides it into different features and selection extraction.


An unsupervised learning algorithm is the training of a machine through unidentified and unclassified data. From this data, the algorithm figures out the patterns and similarities and make various groups. This algorithm is different from than supervised algorithm in a way that it does not require any supervision for learning. For instance, if you provide the model some pictures of cats and dogs, it will categorize the features of those pictures and make groups of cats and dogs depending on similarities and dissimilarities.


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