Python programming integrated throughout for hands-on learning and reduced manual calculations
Covers advanced topics such as ANOVA, multivariate models, and machine learning algorithms
Uses real-world data science tools from the sklearn package for practical examples
Clear, step-by-step code explanations make concepts accessible to beginners and advanced learners
Designed for undergraduate and first-year graduate students in various disciplines
Applied Statistics with Python, Volume II focuses on ANOVA, multivariate models such as multiple regression, model selection, and reduction techniques, regularization methods like lasso and ridge, logistic regression, K-nearest neighbors (KNN), support vector classifiers, nonlinear models, tree-based methods, clustering, and principal com
Format
Digital ebook
Language
English
Content
Volume II covers ANOVA, multivariate models, machine learning, and more
Tools Used
Python and scikit-learn (sklearn) package
Target Audience
Undergraduate and first-year graduate students, professionals with basic statistics knowledge
