Post by Author : nisha-arya

The Random Forest algorithm is a versatile and powerful tool capable of handling various data-driven challenges for machine learning. The concept of Random Forest took birth because of the need for simplicity and ensemble learning. In Layman’s terms, Ensemble Learning is stacking together a lot of classifiers to improve performance. What is a Random Forest? Random Forests is a Supervised

Before we discuss Artificial Narrow Intelligence, let’s dive into this article; let’s quickly recap the definition of Artificial Intelligence: Artificial Intelligence (AI) is the ability of a computer or a computer-controlled robot to perform tasks that humans usually do as they require human intelligence. There are three types of Artificial Intelligence (AI): In this article, we will be going over

K-nearest neighbors (k-NN) is a Machine Learning algorithm for supervised machine learning type. It is used for both regression and classification tasks. As we already know, a supervised machine learning algorithm depends on labeled input data, which the algorithm learns to produce accurate outputs when input unlabeled data. k-NN aims to predict the test data set by calculating the distance

If you read the “An Introduction to Clustering” article, you will know that Hierarchical Clustering is a type of Connectivity model in Machine Learning. To recap, Connectivity Models are based on the fact that data points in the same data place have similarities. What is Hierarchical Clustering? Hierarchical Clustering is an algorithm that groups similar data points into clusters. Hierarchical

K-means clustering is a type of unsupervised learning when we have unlabeled data (i.e., data without defined categories or groups). Clustering refers to a collection of data points based on specific similarities. K-Means Algorithm K-means aims to find groups in the data, with the number of groups represented by the variable K. Based on the provided features, the algorithm works

When we cluster things, we put them into groups. In Machine Learning, Clustering is the process of dividing data points into particular groups. One group will have similar data points and differentiate from those with other data points. It is purely based on the patterns, relationships, and correlations in the data. Clustering is a form of Unsupervised Learning. Let’s quickly

In the ever-evolving landscape of technology, the pursuit of Artificial General Intelligence (AGI) stands as one of our time’s most ambitious and consequential endeavors. AGI aims to create machines with the ability to understand, learn, and apply intelligence as flexibly and robustly as a human. Therefore, it is not just a milestone in artificial intelligence; it is a potential turning