Introduction Gaussian Mixture Models (GMMs) are a popular probabilistic model used for representing a mixture of several Gaussian distributions. GMMs are highly effective for modeling data that exhibits multiple underlying subpopulations, especially in unsupervised learning tasks such as clustering, density estimation, and anomaly detection. They are used to approximate complex, multi-modal distributions, making them…

Introduction Metric learning is a subfield of machine learning that focuses on learning a distance function that quantifies the similarity or dissimilarity between data points. Unlike traditional machine learning models that typically use fixed, pre-defined metrics (such as Euclidean distance), metric learning aims to learn the best metric that captures the underlying structure of…

Introduction Semi-supervised learning (SSL) is a machine learning paradigm that combines both labeled and unlabeled data to improve the learning process. In traditional supervised learning, models are trained on a fully labeled dataset, where each input comes with a corresponding output. However, obtaining labeled data is often expensive, time-consuming, and labor-intensive, especially in complex…

Introduction Bayesian Networks (BNs) are probabilistic graphical models that represent a set of variables and their conditional dependencies using a directed acyclic graph (DAG). These models provide a way of representing complex relationships in data through conditional probabilities. Bayesian Networks have been widely used in various fields such as artificial intelligence (AI), machine learning…

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…

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…

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…

Introduction Gradient Boosting algorithms, such as XGBoost (Extreme Gradient Boosting) and LightGBM (Light Gradient Boosting Machine), are among the most powerful machine learning techniques used for both classification and regression tasks. These algorithms build strong predictive models by combining multiple weak models (usually Decision Trees) in an additive manner. They focus on minimizing errors…

Introduction Decision Trees and Random Forests are powerful machine learning algorithms widely used for both classification and regression tasks. These models are intuitive, easy to interpret, and capable of handling complex datasets with minimal preprocessing. While Decision Trees provide a simple and transparent approach, Random Forests enhance their performance by creating an ensemble of…

Introduction Support Vector Machines (SVMs) are powerful supervised machine learning models used primarily for classification and regression tasks. Introduced in the 1990s, SVMs have since become one of the most popular techniques in machine learning, known for their efficiency in handling complex, high-dimensional data. SVMs work by finding the hyperplane that best divides a…