Machine Learning

Machine learning is a branch of artificial intelligence (AI) that involves developing algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data. Unlike traditional programming, where explicit instructions are provided to solve a specific task, machine learning algorithms use data to learn patterns and relationships, allowing them […]

Supervised learning is a type of machine learning where the model is trained on a labeled dataset, meaning that each input data point is paired with the correct output label. The goal is for the model to learn a mapping from inputs to outputs, which can then be used to make predictions on new, unseen

In the field of machine learning, decision trees are a powerful tool for classification and regression tasks. They are versatile, easy to interpret, and can handle both numerical and categorical data. In this article, we will explore the decision tree algorithm, how it works, and how to implement it in Python. What is a Decision

Support Vector Machine (SVM) is a powerful supervised machine learning algorithm used for classification and regression tasks. It’s particularly effective in high-dimensional spaces and for cases where the number of dimensions exceeds the number of samples. In this article, we’ll explore how SVM works, its key concepts, and its applications. 1. Basic Concepts of SVM

The K-Nearest Neighbors (KNN) algorithm is a simple yet powerful supervised machine learning algorithm used for classification and regression tasks. It is a non-parametric and instance-based learning algorithm, meaning it does not make any assumptions about the underlying data distribution and stores instances of the training data itself to make predictions. In this article, we