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 ...
Evaluating the performance of machine learning models is crucial, especially for classification tasks, where a model predicts one of several classes. While metrics like accuracy give an overview, they don’t provide a complete picture, especially when the data is imbalanced. This is where the confusion matrix plays a significant role. A confusion matrix is a ...
Big data refers to the vast amount of structured and unstructured data generated from sources like social media, IoT devices, and digital transactions. It plays a crucial role in today’s digital world, helping organizations analyze trends, forecast outcomes, and make informed decisions. Big data enables industries to personalize services, optimize operations, and drive innovation. Sectors ...
Mohit Uniyal
Mayank Gupta
Team Applied AI