Kernel methods are a class of machine learning algorithms that enable efficient data transformation into higher-dimensional spaces without explicitly computing those dimensions. They are widely used in tasks such as classification and regression, particularly in Support Vector Machines (SVMs) and Kernel Ridge Regression, to capture complex patterns in data. Understanding Kernel Methods Kernel methods are ...
Reasoning in artificial intelligence is the process of deriving logical conclusions from available data, rules, and prior knowledge. It enables AI systems to make informed decisions, solve problems, and simulate human-like intelligence. Reasoning is essential in expert systems, decision-making, and automated problem-solving, allowing AI to infer new knowledge, validate information, and adapt to dynamic environments. ...
Correlation analysis in data mining is a statistical method used to measure the strength and direction of relationships between variables. It helps identify patterns and dependencies within datasets, making it useful for predictive modeling, feature selection, and trend analysis. However, correlation only indicates an association and does not imply causation. What is Correlation Analysis? Correlation ...
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 ...
Mohit Uniyal
Mayank Gupta
Team Applied AI