All About Artificial Intelligence
Recommender systems are an important application of artificial intelligence (AI) that help users discover relevant items or content based on their preferences, interests, or behavior. Various domains, including e-commerce, entertainment, social media, and more, actively employ these systems, showcasing their widespread utilization. There are several approaches to building recommender systems, including collaborative filtering, content-based filtering,…
The K Nearest Neighbor (KNN) algorithm is a simple and intuitive supervised machine learning algorithm. It is used for both classification and regression tasks. K Nearest Neighbor (KNN) works based on the assumption that similar data points tend to belong to the same class. Here’s how the KNN algorithm works: The choice of K is…
Ensemble learning is a machine learning technique that involves combining multiple models, called base learners or weak learners. Using ensemble learning builds a more accurate and robust predictive model. The idea behind ensemble learning is that by combining the predictions of multiple models. The resulting ensemble model can achieve better performance than any individual model….
Cluster analysis is a technique used in data analysis and machine learning to identify groups or clusters within a dataset. It is an unsupervised learning method that aims to find similarities and patterns in the data without prior knowledge of the group assignments. The goal of cluster analysis is to partition a dataset into subsets,…
Image classification with the CIFAR-10 dataset is a popular task in computer vision and machine learning. The CIFAR-10 dataset consists of 60,000 color images (32×32 pixels) across 10 different classes, with 6,000 images per class. Performing image classification with the CIFAR-10 dataset involves several general steps and some important steps are here below:
In Machine Learning, A/B testing is also known as split testing, organizations utilize this technique to compare and determine the performance of different versions of a system or strategy. The process involves dividing a group of users or participants into multiple groups and exposing them to various variants (A or B) of a specific feature,…
In machine learning, the F1 score is a widely used metric for evaluating the performance of a binary classification model. It offers a balanced measure by combining precision and recalls into a single score. It is calculated using the formula: F1 score = 2 * (precision * recall) / (precision + recall) Precision and recall…
In machine learning, overfitting and underfitting are two common problems that can occur when training a model. They are related to the model’s ability to generalize its predictions to unseen data. Here’s an explanation of each term: Signs of overfitting include: To mitigate overfitting, you can try the following: Signs of underfitting include: To address underfitting,…
Definition and Purpose: Computational learning theory is a branch of theoretical computer science that focuses on mathematically analyzing learning algorithms. Its goal is to understand the principles and limitations of machine learning, providing a theoretical foundation for studying the efficiency, accuracy, and generalization properties of learning algorithms. Key Concepts in Computational Learning Theory 1. Learning…