Probability
Dive into the world of probability with this comprehensive 1.5-hour tutorial covering key concepts in data science and machine learning. Explore Bayes Theorem and Hidden Markov Models, gaining insights into their applications. Uncover the principles of Shannon Entropy and Information Gain, essential for understanding data compression and decision trees. Learn about the Naive Bayes classifier through a user-friendly approach, perfect for beginners and experienced learners alike. Discover the Beta distribution in just 12 minutes, grasping its importance in Bayesian statistics. Finally, delve into Thompson sampling, one-armed bandits, and their connection to the Beta distribution, enhancing your understanding of probabilistic algorithms and decision-making under uncertainty.