Breadth-first search (BFS) It is a simple search strategy where the root node is expanded first, then covering all other successors of the root node, further move to expand the next level nodes and the search continues until the goal node is not found. BFS expands the shallowest (i.e., not deep)Continue Reading
The reflex agents are known as the simplest agents because they directly map states into actions. Unfortunately, these agents fail to operate in an environment where the mapping is too large to store and learn. Goal-based agent, on the other hand, considers future actions and the desired outcomes. Here, we will discuss one typeContinue Reading
What is an Agent? An agent can be viewed as anything that perceives its environment through sensors and acts upon that environment through actuators. For example, human being perceives their surroundings through their sensory organs known as sensors and take actions using their hands, legs, etc., known as actuators. DiagrammaticContinue Reading
Introduction to Artificial Intelligence Applications Artificial Intelligence Application is a subject widely prevalent in the IT world for more than 4 decades and it was in the laboratory all through. In today’s digital world, abundant availability of Data generated from multiple sources (Big data) Compute and storage resources (on-premises and Cloud)Continue Reading
The main aim of Artificial Intelligence aim is to enable machines to perform a human-like function. Thus the primary way of classification of AI is based on how well it is able to replicate human-like actions. AI can, by and large, be classified based on two types, both of which are basedContinue Reading
What is Artificial Intelligence? The term Artificial Intelligence comprises of two words ‘Artificial’ and ‘Intelligence’, where, Artificial means ‘copy of something natural’ and ‘Intelligence’ means ‘able to think.’ So, Artificial Intelligence can be defined as a copy of a human brain with thinking ability. According to John McCarthy, who is known as theContinue Reading
Introduction to Association Rule Learning Association rule learning extracts alliances among the datapoints in a huge dataset. It incorporates the concept of data mining, which helps in finding useful commercial associations or regularities between the variables. It is presently in use in the sales industry to predict if the personContinue Reading
Introduction to Hierarchical Clustering The other unsupervised learning-based algorithm used to assemble unlabeled samples based on some similarity is the Hierarchical Clustering. There are two types of hierarchical clustering algorithm: 1. Agglomerative Hierarchical Clustering Algorithm It is a bottom-up approach. It does not determine no of clusters at the start. ItContinue Reading
Introduction to K-means clustering K-mean clustering comes under the unsupervised based learning, is a process of splitting an unlabeled dataset into the clusters based on some similarity patterns present in the data. Given a set of m nos. of the data item with some certain features and values, the mainContinue Reading
Introduction to ML Clustering Algorithm Clustering falls under unsupervised learning methods. In this, the machine is provided with a set of unlabeled data, and the machine is required to extract the structure from the data from its own, without any external supervision. It searches for similar patterns in the datasetContinue Reading
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