Covers fundamental concepts in probability and statistics, including the law of total probability, Bayes' theorem, and independence of events.
Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.
