Getting Started in Machine Learning: Easy Recipes for Python 3, Scikit-Learn, and Jupyter

This is an introductory book in machine learning with a hands on approach. It uses Python 3 and Jupyter notebooks for all applications. The emphasis is primarily on learning to use existing libraries such as Scikit-Learn with easy recipes and existing data files that can found on-line. Topics include linear, multilinear, polynomial, stepwise, lasso, ridge, and logistic regression; ROC curves and measures of binary classification; nonlinear regression (including an introduction to gradient descent); classification and regression trees; random forests;  neural networks; probabilistic methods (KNN, naive Bayes', QDA, LDA); dimensionality reduction with PCA; support vector machines; and clustering with K-Means, hierarchical, and DBScan. Appendices provide a review of probability and linear algebra. While some mathematical foundation is provided, it is not essential for understanding the implementations. The target audience is advanced community college students and intermediate university students in the sciences and engineering. 

This book is a pdf -version of the printed text, with the exact same page-for-page layout. It is made available here as a convenience for readers who prefer to read the pdf version instead of the paper version. The book was designed for paper and is incompatible with free-form ereaders, but is compatible with most pdf-readers and tablets.