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Introduction In the realm of machine learning, online learning refers to algorithms that learn incrementally, processing one data point at a time. This stands in contrast to batch learning, where the model is trained on the entire dataset at once. Online learning is particularly valuable in situations where the data is too large to…
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Introduction Clustering is a type of unsupervised machine learning technique used to group similar data points together. It plays a pivotal role in various machine learning applications, including anomaly detection, data compression, and market segmentation. One of the most powerful clustering algorithms is DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which groups data…
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Introduction Active learning is a machine learning paradigm that is used to solve problems where labeled data is scarce or expensive to obtain. In traditional machine learning, a model is trained on a large, fully labeled dataset. However, in many real-world scenarios, labeling data is time-consuming and expensive, particularly when expert knowledge is required.…
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Introduction Ensemble learning is a powerful concept in machine learning where multiple models (often called “learners”) are combined to improve the overall performance of a model. Instead of relying on a single model, ensemble methods leverage the collective knowledge of several models to achieve better predictive performance, robustness, and generalization. This approach is especially…
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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…
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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…
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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…
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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…
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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…
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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…