Introduction The K-Nearest Neighbors (KNN) algorithm is one of the simplest and most intuitive machine learning algorithms used for classification and regression tasks. It is a non-parametric method, meaning it makes no assumptions about the underlying data distribution. Instead, KNN classifies new data points based on the majority class (for classification) or the average of
Introduction Stochastic Gradient Descent (SGD) is one of the most widely used optimization algorithms in machine learning, particularly for training large-scale models such as deep neural networks. SGD is an iterative method used to minimize a loss function by adjusting the model parameters in the direction of the negative gradient. This makes it an essential
Introduction Principal Component Analysis (PCA) is a powerful statistical technique widely used for dimensionality reduction and feature extraction in machine learning. It is particularly useful when dealing with high-dimensional data, where the number of features can be overwhelming and may lead to challenges such as overfitting, computational inefficiency, and interpretability issues. PCA helps mitigate these