In machine learning, statistical models often rely on hidden information or latent variables—elements of data that are not directly observed but influence the overall outcomes. Identifying the optimal parameters for these models becomes challenging when such latent variables are present. The Expectation-Maximization (EM) algorithm offers a powerful solution to this problem. It is designed to ...
What Is Anomaly Detection? Anomaly detection in machine learning identifies unusual patterns in data that may indicate issues like fraud, security breaches, or equipment failures. Detecting these anomalies early allows organizations to take preventive measures, enhancing safety and efficiency. Types of anomalies include: Anomaly detection is widely used in fields like finance, healthcare, and system ...
Quantum Machine Learning (QML) is an exciting and emerging field that combines quantum computing and machine learning. While classical machine learning has made great strides, it faces limitations, especially when solving highly complex problems that require enormous computational power. This is where QML comes into play—it uses the principles of quantum mechanics to potentially solve ...
Gradient Descent is one of the most important optimization algorithms in the field of machine learning. Optimization algorithms are used to minimize or maximize a function, which is crucial for training models effectively. Gradient Descent helps find the best parameters (weights and biases) for a model by reducing the error in predictions step by step. ...
Artificial Intelligence (AI) is a vast field that focuses on creating machines capable of performing tasks that typically require human intelligence. Two critical areas of AI are machine learning (ML) and deep learning (DL). These technologies have revolutionized industries like healthcare, finance, and entertainment. But what exactly sets them apart? Both machine learning and deep ...
Machine learning has revolutionized numerous industries, providing systems the ability to learn from data and improve over time without explicit programming. One of the earliest and most fundamental algorithms in machine learning is the Perceptron model. Developed in the late 1950s by Frank Rosenblatt, the Perceptron is historically significant for laying the groundwork for neural ...
In today’s data-driven world, both machine learning and data science have gained massive importance. They help businesses and industries understand data, make predictions, and drive decisions. However, many people often confuse these terms or use them interchangeably. This article will explain the main differences between machine learning and data science in simple terms, ideal for ...
Convolutional Neural Networks (CNNs) are a type of deep learning model commonly used in image recognition tasks. Unlike traditional neural networks, CNNs are designed to automatically detect patterns from images, making them highly efficient in visual data processing. Deep learning, a subset of machine learning, enables machines to mimic the way humans learn from experience, ...
In machine learning, when building models for classification tasks (like predicting whether an email is spam or not), it’s important to evaluate how well the model performs. One of the most useful tools for doing this is the ROC curve. ROC stands for Receiver Operating Characteristic, and this curve helps visualize a model’s ability to ...
Machine learning is all about teaching computers to learn from data and make decisions without being explicitly programmed. One of the key tasks in machine learning is concept learning, which focuses on learning general ideas or concepts from specific examples. By learning these concepts, machines can classify new data accurately, making this technique essential for ...
Mohit Uniyal
Anshuman Singh
Mohit Uniyal