Has AI become a buzzword? In essence, artificial Intelligence (AI) is a way of building computer systems to emulate human intelligence.
In computer science, artificial intelligence (AI) is the field that deals with creating intelligent computer systems, or systems that display traits that humans identify as intelligence in human behaviour, such as language comprehension, learning, reasoning, problem-solving, and so on.– (Barr & Feigenbaum, 1981)
Artificial intelligence may be divided into these areas: fuzzy logic, robotics, expert systems, machine learning, neural networks, and natural language processing (NLP). (https://www.analyticssteps.com/blogs/6-major-branches-artificial-intelligence-ai#google_vignette)
When it comes to machine learning, there are three varieties: reinforcement learning, unsupervised machine learning, and supervised machine learning.
The main focus of this article is to examine unsupervised machine learning. An algorithm for machine learning that uses unlabeled data to train its data is known as unsupervised machine learning.
Unsupervised learning algorithms find hidden relationships or patterns in the data without human assistance. Due to this ability and its capacity to identify patterns in data, it is the best option for exploratory data analysis, consumer segmentation, and picture recognition.
Let us consider a scenario where genes are grouped based on expression patterns to discover groups with similar biological functions. This is an illustrative case study for exploratory data analysis that may be explored with unsupervised learning models.
- Clustering Algorithms
When working with unlabeled data, one way to find hidden patterns is to group the objects into clusters. One cluster is made up of objects with the best similarity, while another is made up of objects with little to no similarity. Various methods, such as K-means clustering, Hierarchical clustering, DBSCAN, Mean Shift, Agglomerative clustering, Gaussian Mixture Models, and others, can assist in achieving this, contingent upon the objectives of the study and the attributes of the data.
- Dimensionality Reduction
When working with unlabeled data, the input variables may be too many to train. A dimensionality reduction algorithm is necessary to reduce the number of input variables. Reducing our data set from a relatively high number of variables to a reduced number is usually termed the degrees of freedom. When reducing the input variables, we must capture all relevant variables, which is very important. Dimensionality reduction is performed after the data has been cleaned and preprocessed.
There are quite some applications of unsupervised machine learning. This could include:
– Image compression and feature extraction
– Anomaly detection in cybersecurity
– Document clustering in text analysis
– Financial fraud detection
– Social network analysis
In the upcoming write-up, we will examine a deep dive into one of the applications.
Thank you for reading. I hope you found it insightful.
P.S: An old article was selected to define Artificial Intelligence to help users understand that the fundamental concept behind Artificial Intelligence has not changed even though many evolutionary techniques have emerged.
Happy new year in advance to all readers