XGBoost (Extreme Gradient Boosting) is a powerful machine learning algorithm designed for structured data. It is widely used in real-world applications due to its speed, efficiency, and superior predictive performance. Developed by Tianqi Chen, XGBoost optimizes traditional gradient boosting by incorporating regularization, parallel processing, and efficient memory usage. This algorithm has gained immense popularity in ...
Nominal data is a type of categorical data that represents labels or names without any inherent order. It is used for classification rather than numerical analysis, meaning the values cannot be ranked or measured. In data analysis, nominal data is essential for organizing and labeling information, making it useful for fields like market research, healthcare, ...
Problem formulation in artificial intelligence (AI) is the process of structuring a real-world issue into a well-defined computational problem that an AI system can solve. It is a critical step in AI development, as it defines the objectives, constraints, and possible actions the AI agent can take to reach an optimal solution. By formulating a ...
Artificial Intelligence (AI) is revolutionizing industries by automating tasks, enabling smarter decision-making, and solving complex problems. From healthcare diagnostics to financial fraud detection and personalized e-commerce experiences, AI is reshaping the way businesses operate. As AI adoption grows, the demand for skilled professionals in AI-driven roles is skyrocketing. A recent report by Gartner predicts the ...
Bagging, short for Bootstrap Aggregating, is a popular ensemble learning technique in machine learning. It works by combining predictions from multiple models to reduce variance, enhance stability, and improve overall performance. By training models on randomly sampled subsets of data and aggregating their outputs, Bagging minimizes the risk of overfitting and increases generalization. This article ...
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 ...
Team Applied AI
Abhimanyu Saxena
