In today’s data-driven world, efficient data management plays a pivotal role in driving business success. Organizations rely on structured systems to store, organize, and retrieve data for decision-making and operations. Two commonly used tools in this domain are databases and data warehouses. A database is designed to handle real-time transactional data, supporting day-to-day operations, while ...
A data warehouse is a centralized repository designed to store and manage large volumes of structured data gathered from multiple sources. It serves as the foundation for advanced analytics, enabling organizations to query, analyze, and extract valuable insights from their data. Unlike traditional operational databases, which are optimized for real-time transactional processing (OLTP), data warehouses ...
Artificial Intelligence (AI) governance is a vital framework designed to ensure the ethical and responsible development, deployment, and management of AI systems. As AI adoption accelerates globally—projected to contribute over $15.7 trillion to the world economy by 2030 (PwC, 2021)—the need for robust governance becomes increasingly urgent. This framework addresses key challenges, including algorithmic bias, ...
Artificial intelligence (AI) has revolutionized software development by enhancing productivity, reducing errors, and simplifying complex tasks. AI-powered tools assist developers at every stage, from code writing to debugging, significantly streamlining workflows. With the growing demand for software solutions, these tools are becoming indispensable for developers at all skill levels. This article explores AI-powered coding assistant ...
Instance-based learning is a type of machine learning that focuses on storing the training data and comparing new instances to these stored examples to make predictions. Unlike other methods, it does not rely on creating an explicit model during training. Instead, decisions are made based on similarities between new and existing data points. This approach ...
Data Science and Artificial Intelligence (AI) are two of the most sought-after fields in technology today. While they share similarities, each plays a unique role in solving complex problems and driving innovation. With industries increasingly relying on data-driven insights and intelligent systems, understanding the distinction between these fields is essential. In this article, we will ...
Artificial Intelligence (AI) is revolutionizing the entertainment industry, reshaping how content is created, distributed, and consumed. From personalized recommendations on platforms like Netflix and Spotify to AI-generated scripts, music, and artwork, the integration of AI is driving unprecedented innovation. It allows businesses to optimize workflows, engage audiences effectively, and enhance creative processes. In gaming, AI ...
Pruning is a crucial optimization technique in machine learning that simplifies models by removing unnecessary components, such as nodes in decision trees or weights in neural networks. This technique helps: Pruning is widely used in decision trees, neural networks, and support vector machines (SVMs). This article explains what pruning is, its types, and its practical ...
Ontological engineering is a crucial part of Artificial Intelligence (AI) that focuses on creating structured frameworks to represent knowledge. It helps AI systems understand, manage, and connect complex information efficiently. In simple terms, ontologies provide a formal way to describe concepts, relationships, and data within a particular domain. They are widely used in areas like ...
In machine learning, data is the backbone of successful model training. However, data scarcity and imbalances often hinder model performance, leading to overfitting or poor generalization. This is where data augmentation comes in. By applying various transformations to existing datasets, data augmentation enhances data quality and diversity, creating synthetic data while preserving the core characteristics ...
Team Applied AI
Mayank Gupta
Anshuman Singh