High-dimensional data, which involves datasets with many features, is common in machine learning today. While these features can offer valuable insights, they also introduce challenges, known as the curse of dimensionality. As dimensions increase, data points become sparse, making it difficult for algorithms to identify patterns. This can result in issues like overfitting, higher computational ...
In the realm of Artificial Intelligence (AI), search algorithms are essential for solving problems where the path to a solution is not immediately clear. Search algorithms are divided into two categories: uninformed and informed. Uninformed search algorithms, also known as blind search algorithms, explore the search space without any prior knowledge about the goal or ...
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 ...
Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks typically requiring human intelligence. These tasks range from problem-solving and decision-making to understanding natural language and recognizing patterns. AI is not just a futuristic concept—it is already part of our daily lives, powering technologies like virtual assistants, recommendation algorithms, and facial ...
Planning is a fundamental aspect of Artificial Intelligence (AI), enabling systems to solve complex problems and achieve goals autonomously. In AI, planning refers to the process of formulating a sequence of actions to transition from an initial state to a desired goal state. By leveraging planning, AI systems can navigate uncertain environments, optimize resource usage, ...
The field of Artificial Intelligence (AI) is rapidly expanding, with applications in industries such as healthcare, finance, retail, and transportation. As companies increasingly adopt AI to drive innovation, the demand for skilled AI engineers has skyrocketed. AI engineers design, develop, and implement AI-powered systems, making them vital in shaping the future of technology. With an ...
Machine learning models are built on a foundation of data and algorithms that process, analyze, and extract insights from that data. Behind the scenes, many of these machine learning algorithms rely heavily on mathematical concepts to function effectively. One of the most important mathematical fields for machine learning is **linear algebra**. Linear algebra deals with ...
In machine learning, models rely heavily on the features of a dataset to make accurate predictions. However, more data does not always lead to better results. This is where feature selection becomes critical. It is a process that involves selecting a subset of the most relevant features, helping to improve model performance, reduce training time, ...
Becoming a data scientist is a rewarding career path, and starting early, right after 12th grade, can give you a significant head start. In today’s data-driven world, data scientists play a crucial role in analyzing complex data to derive meaningful insights for businesses. By focusing on the right subjects in school, learning essential programming languages, ...
The Mini-Max algorithm is a cornerstone in artificial intelligence, particularly in decision-making processes for two-player games. It plays a crucial role in game theory by allowing AI agents to select optimal moves assuming their opponents are also playing optimally. This strategy helps minimize the possible losses (for the “min” player) and maximize gains (for the ...
Mohit Uniyal
Mayank Gupta
Team Applied AI