Linear regression is a supervised learning algorithm that is used to analyze the relationship between a dependent variable and one or more independent variables. It is a widely used method in machine learning and statistics for both simple and multiple linear regression. The goal of linear regression is to find the best-fitting line that represents…
k-Nearest Neighbors (k-NN) is a non-parametric, instance-based supervised learning algorithm used for classification and regression. It is used to identify the k-number of closest training examples in the feature space for a given unseen observation. The output for the observation is then set to the mode of the k nearest neighbors’ output variable. The k-NN…
Supervised learning is a type of machine learning in which an algorithm is trained on a labeled dataset to make predictions or take actions. The goal of the algorithm is to learn a mapping from input data to output labels, such that it can make accurate predictions on new, unseen data. Types of Supervised Learning…
Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that enables systems to learn from data and make predictions or decisions without explicit programming. It is a key technology for the development of intelligent systems and has been applied in various domains such as image recognition, natural language processing, and autonomous vehicles. The Basics…
Neural networks are a fundamental aspect of Artificial Intelligence (AI) research. They are a type of machine learning algorithm modeled after the structure and function of the human brain. Neural networks have been successfully applied in a variety of fields, including image and speech recognition, natural language processing, and decision-making. In this article, we will…
Deep learning, a subfield of machine learning, has been a significant area of research in the field of artificial intelligence. It involves the use of neural networks, which are modeled after the human brain, to analyze and process vast amounts of data. The advancements in deep learning have led to the development of powerful models…
