Reinforcement Learning (RL) is a powerful area of artificial intelligence that enables systems to learn and adapt through a process of trial and error, aiming to achieve specific goals. Unlike traditional learning methods, RL emphasizes learning optimal actions based on feedback from the environment. This approach is especially valuable for tasks where defining explicit rules ...
In artificial intelligence, search algorithms are key tools for exploring possible solutions to complex problems. Among these, Uniform Cost Search (UCS) stands out as a fundamental type of uninformed search algorithm. Unlike informed search methods, UCS does not use heuristics; instead, it focuses on finding the least-cost path by expanding nodes based on the cumulative ...
Machine learning models rely on two types of configurations: parameters learned during training and hyperparameters that need to be manually set. Hyperparameters, such as learning rate in neural networks or C value in Support Vector Machines (SVMs), directly impact how well a model performs. Setting them incorrectly can result in underfitting or overfitting, making it ...
Polynomial regression is an essential extension of linear regression used to model non-linear relationships in data. In many real-world scenarios, the relationship between variables isn’t linear, making polynomial regression a suitable alternative for achieving better predictive accuracy. This technique allows machine learning models to capture curved patterns in data by fitting polynomial equations of higher ...
Classification is one of the core tasks in machine learning, enabling models to predict discrete outcomes based on input data. This supervised learning technique assigns data points to predefined categories or classes. Classification algorithms power many of the automated systems we use daily, from email spam filters to fraud detection systems in banking. The importance ...
Understanding the difference between classification and regression is crucial for solving machine learning problems effectively. Both tasks involve making predictions based on data, but they differ in their output type and the algorithms used. Selecting the right approach ensures accurate results and better decision-making for various applications. What is Classification? Classification in machine learning is ...
Q-learning is a fundamental algorithm in the field of reinforcement learning (RL), a type of machine learning that focuses on training agents to make sequential decisions through trial and error. In RL, the agent interacts with its environment, learning to achieve a goal by maximizing cumulative rewards over time. This concept mimics how humans and ...
Artificial Intelligence (AI) is about building machines and systems that can reason, learn, and solve problems, similar to how humans do. In many AI applications, logical reasoning plays a crucial role, allowing systems to make decisions based on given conditions and facts. Propositional Logic is a fundamental part of AI that deals with reasoning using ...
A Bayesian Belief Network (BBN) is a type of probabilistic graphical model that represents a set of variables and their conditional dependencies through a directed acyclic graph (DAG). These networks are powerful tools for reasoning under uncertainty and are widely used in artificial intelligence (AI) applications. BBNs are built using Bayes’ theorem, which allows the ...
Machine Learning (ML) has evolved from philosophical concepts about artificial intelligence into a foundational technology of the modern era. It has undergone multiple phases, from early neural networks to today’s deep learning models. Understanding ML’s history provides insight into how it has grown to impact industries and everyday life. What is Machine Learning? Machine Learning ...
Team Applied AI
Anshuman Singh
