Introduction to Markov Chains

Do you want to understand how to model systems that evolve over time based on probabilities? This course provides a comprehensive introduction to Markov Chains, equipping you with the theoretical knowledge and practical understanding to analyze sequential data and predict system behavior. What you'll learn: * Learn the fundamental definitions and terminology of Markov Chains * Understand how to construct and interpret transition probability matrices * Analyze key properties such as stationarity, irreducibility, and periodicity * Apply Markov Chains to model real-world scenarios through practical examples * Explore the role of Markov Chains in algorithms for sequential decision-making and basic data science applications * Practice calculations for steady-state distributions and expected returns The course begins by establishing essential definitions and gradually introduces core concepts, building up to analyzing advanced properties and exploring practical applications through clear, written explanations and practice exercises. This course is ideal for beginners eager to understand probabilistic modeling, with no prior experience in Markov Chains required. Enroll today to start your journey into the fascinating world of Markov Chains.