Gradient descent is an optimization method used to find the minimum value of a function by iteratively updating the parameters of the function.
Principal component analysis is a method used to reduce the number of dimensions in a dataset without losing much information.
Simple linear regression is a statistical method you can use to study relationships between two continuous (quantitative) variables: independent variable (x) – also referred to as predictor or explanatory variable dependant variable (y) – also referred to as response or outcome The goal of any regression model is to predict the value of y (dependant variable) based on the value of x (independent variable).
Binomial distribution is used to understand the probability of a particular outcome in repeated independent trials.
Markow chain is a probabilistic process used to predict the next step based on the probabilities of the existing related states.