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How Are Probabilistic Models Used In Machine Learning? Have you ever wondered how computers make predictions and decisions when faced with uncertain information? In this engaging video, we'll explain how probabilistic models are used in machine learning to interpret and manage uncertainty. We’ll explore how these models help computers understand complex data, improve their predictions, and adapt as new information becomes available. You'll learn about the core concepts behind probabilistic models, including probability distributions and how they relate to real-world data. We’ll introduce common types of models like Bayesian networks, Markov models, and Gaussian mixture models, explaining their roles and how they work in practical applications. These models are essential for tasks such as spam detection, house price prediction, fraud detection, and even self-driving cars. They allow machines to weigh different possibilities and make smarter, more reliable decisions by considering the likelihood of various outcomes. Throughout the video, we’ll discuss the historical significance of probability in society and how it continues to influence modern technology. Whether you're interested in data science, artificial intelligence, or simply curious about how machines understand the world, this video provides a clear overview of the vital role probabilistic models play in machine learning.

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