Machine Learning (ML) is a branch of Artificial Intelligence (AI) that focuses on enabling computers to learn from data without being explicitly programmed. Think of it as teaching a computer to recognize patterns and make predictions or decisions based on the data it encounters. With the growth of data and computing power, machine learning has ...
Preparing for a machine learning interview can be challenging, especially for those new to the field. To help you succeed, this guide covers over 100 machine learning interview questions, categorized by experience level: Beginner, Intermediate, and Advanced. Whether you’re starting your journey or advancing your career, these questions provide a solid foundation across various machine ...
Dimensionality reduction is a technique used in machine learning to simplify complex, high-dimensional data. As data grows in size and complexity, it often contains many features (variables), making it challenging to process. This high-dimensional data can lead to problems like the curse of dimensionality, where the performance of models deteriorates due to too many features. ...
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 ...
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, ...
Team Applied AI
Abhimanyu Saxena
