Ensemble learning is a popular approach in machine learning where multiple models are combined to improve the accuracy and robustness of predictions. Often, individual models may have limitations, such as overfitting or underfitting. By combining several models, ensemble methods can reduce these issues and produce better results. Stacking (stacked generalization) is an ensemble technique that ...
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 ...
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 ...
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 ...
Artificial Intelligence (AI) has rapidly become one of the most transformative technologies of the 21st century. From virtual assistants like Siri and Alexa to recommendation systems on Netflix and Amazon, AI is everywhere. Its applications span across industries such as healthcare, finance, transportation, and retail, making it an essential skill for future professionals. Whether you’re ...
Data has become one of the most valuable resources in today’s world, powering everything from business decisions to product recommendations. With every passing second, more data is being generated across various platforms and industries, making it essential to analyze and interpret this information effectively. Data analytics plays a crucial role in helping organizations process large ...
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, ...
Collaborative filtering is a core technique used in recommendation systems. It plays a crucial role in personalizing experiences for users on platforms such as e-commerce sites, streaming services, and social media networks, improving engagement by suggesting relevant items based on user behavior patterns. What is Collaborative Filtering? Collaborative filtering is a method used to predict ...
In machine learning, models primarily work with numerical data. However, many real-world datasets include categorical variables, such as colors, locations, or types of products. To build effective machine learning models, it’s essential to preprocess these categorical features and transform them into a format that algorithms can interpret. One-hot encoding is a popular method for converting ...
In machine learning, raw data in its initial form often contains noise, irrelevant information, or excessive dimensionality, making it challenging to use directly in models. This is where feature extraction plays a crucial role. It involves transforming raw data into a more informative and usable format, which enhances model performance and reduces computational costs. For ...
Team Applied AI
Anshuman Singh
