Lecture 11-1: Markov Chain Model Explained with Examples

In **Lecture 11: Markov Chain Model**, we explore how Artificial Intelligence models sequences of events and predicts future states using probability. In this lecture, you will learn the fundamentals of Markov Chains, understand the Markov property, and see how transition probabilities help predict future states. We also explain how a Markov Chain generates a sequence of states using a simple weather prediction example in Python. *Topics covered in this lecture:* What is a Markov Chain Model? Understanding the Markov Property States and Transition Probabilities Transition Probability Tables Initial State Distribution How Markov Chains Generate Sequences Weather Prediction Example: Sunny vs. Rainy Introduction to Markov Chains in Python Applications of Markov Models in Artificial Intelligence This lecture is useful for students studying Artificial Intelligence, Machine Learning, Probability, and related computer science courses. 📚 *Subscribe for more lectures* on Artificial Intelligence, Computer Vision, Machine Learning, Deep Learning, and Generative AI. #MarkovChain #ArtificialIntelligence #CS50AI #Probability #MachineLearning