Math for Data Science

Math for Data Science covers the elements of linear algebra, probability, statistics, and calculus most relevant to data science. Applications include dimensionality reduction, machine learning, optimization techniques, neural network training, stochastic gradient descent, square and logistic regression, and accelerated methods.

Throughout, Python code is woven into the narrative, to be experienced in vivo --- alive within context, not examined in vitro. All code is posted above under Files. Also included are nine appendices providing background material and additional context, and 468 exercises.