Textbook in PDF format

This textbook introduces the fundamental concepts and methods of statistical learning. It uses Python and provides a unique approach by blending theory, data examples, software code, and exercises from beginning to end for a profound yet practical introduction to statistical learning. The book consists of three parts: The first one presents data in the framework of probability theory, exploratory data analysis, and unsupervised learning. The second part on inferential data analysis covers linear and logistic regression and regularization. The last part studies Machine Learning with a focus on support-vector machines and Deep Learning. Each chapter is based on a dataset, which can be downloaded from the book's homepage.
In addition, the book has the following features
A careful selection of topics ensures rapid progress.
An opening question at the beginning of each chapter leads the reader through the topic.
Expositions are rigorous yet based on elementary mathematics.
More than two hundred exercises help digest the material.
A crisp discussion section at the end of each chapter summarizes the key concepts and highlights practical implications.
Data Science is the art of turning data into information. Conducting data science on the basis of statistical principles is called statistical learning. Statistical learning establishes systematic rules for data and describes deviations from these rules. This book introduces the foundations of statistical learning and the most common methods. It consists of three parts:
Data are the basis for statistical learning. Our mathematical framework for data is probability theory, which models variations in the data in line with our intuitive notion of chance. These data are then described by basic summary statistics (averages, number of samples...), visualizations (histograms, q-q plots...), and unsupervised learning (PCA, k-means...).
Fundamentals of Data
Exploratory Data Analysis
Unsupervised Learning
Inferential Data Analyses draw conclusions about the data-generating process. The hearts of these analyses are statistical models, such as linear- and logistic regression models.
Linear Regression
Logistic Regression
Regularization
Machine Learning makes predictions about new data. These predictions can involve statistical models, but the models are not the main focus.
Support-Vector Machines
Deep Learning