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Support Vector Machines (SVM) are powerful and versatile machine learning algorithms used for classification and regression tasks . They are particularly popular for their ability to handle complex datasets and produce accurate results. In this blog post, we'll delve into the fundamentals of SVM, how it works, its advantages, and practical applications   What is SVM? •    Support Vector Machine is a supervised learning algorithm that analyse data and recognizes patterns, used for classification and regression analysis. •  SVM aims to find the hyperplane that best separates the classes in the feature space. •   It works by mapping data points to a high-dimensional feature space and finding the hyperplane that best separates the classes with the largest margin. How Does SVM Work? •  SVM works by finding the optimal hyperplane that separates the data into classes. • The optimal hyperplane is the one that maximizes the mar...

Welcome to the fascinating world of Reinforcement Learning (RL), where machines learn to make decisions by trial and error , much like how we humans learn from our experiences. Imagine teaching a computer to play chess, drive a car, or even manage a portfolio of investments. RL is the magic behind these feats, empowering AI agents to navigate complex environments and optimize outcomes through continuous learning. In this article, we'll embark on a journey through RL basics, sprinkled with captivating examples to demystify this cutting-edge field. Imagine you're training a dog (the agent) to perform tricks. Each trick attempted is like an action, and the treats or praise received afterward represent rewards. The environment is the stage where the dog performs, and its current state could be sitting, standing, or rolling over. Your training strategy, akin to the dog's training routine, is the policy guiding which actions to take based on the current state. Ultimately, you aim...