Textbook in PDF format Multivariate Statistics and Machine Learning is a hands-on textbook providing an in-depth guide to multivariate statistics and select machine learning topics using R and Python software. The book offers a theoretical orientation to the concepts required to introduce or review statistical and machine learning topics, and in addition to teaching the techniques, instructs readers on how to perform, implement, and interpret code and analyses in R and Python in multivariate, data science, and machine learning domains. For readers wishing for additional theory, numerous references throughout the textbook are provided where deeper and less “hands on” works can be pursued. With its unique breadth of topics covering a wide range of modern quantitative techniques, user-friendliness, and quality of expository writing, Multivariate Statistics and Machine Learning will serve as a key and unifying introductory textbook for students in the social, natural, statistical, and computational sciences for years to come. Preliminaries and Foundations Introduction, Motivation, Pedagogy, and Ideas About Learning First Principles and Philosophical Foundations Mathematical and Statistical Foundations R and Python Software Models and Methods Univariate and Multivariate Analysis of Variance Models Simple Linear and Multiple Regression Models (and Extensions) Regularization Methods in Regression Ridge, Lasso, and Elastic Net Nonlinear and Nonparametric Regression Generalized Linear and Additive Models Logistic and Related Models Support Vector Machines Decision Trees, Bagging, Random Forests and Committee Machines Principal Components Analysis, Blind Source Separation, and Manifold Learning Exploratory Factor Analysis Confirmatory Factor Analysis, Path Analysis, and Structural Equation Modeling Cluster Analysis and Data Segmentation Artificial Neural Networks and Deep Learning