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A first course in statistical learni...

Lederer, Johannes.

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A first course in statistical learning = with data examples and Python code /

Record Type:	Electronic resources : Monograph/item
Title/Author:	A first course in statistical learning/ by Johannes Lederer.
Reminder of title:	with data examples and Python code /
Author:	Lederer, Johannes.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiv, 282 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Part I: Data -- Chapter 1: Fundamentals of Data -- Chapter 2: Exploratory Data Analysis -- Chapter 3: Unsupervised Learning -- Part II: Inferential Data Analyses -- Chapter 4: Linear Regression -- Chapter 5: Logistic Regression -- Chapter 6: Regularization -- Part III: Machine Learning -- Chapter 7: Support-Vector Machines -- Chapter 8: Deep Learning.
Contained By:	Springer Nature eBook
Subject:	Statistics. -
Online resource:	https://doi.org/10.1007/978-3-031-30276-3
ISBN:	9783031302763

A first course in statistical learning = with data examples and Python code /
Lederer, Johannes.

A first course in statistical learningwith data examples and Python code /[electronic resource] :by Johannes Lederer. - Cham :Springer Nature Switzerland :2025. - xiv, 282 p. :ill. (chiefly color), digital ;24 cm. - Statistics and computing,2197-1706. - Statistics and computing..

Part I: Data -- Chapter 1: Fundamentals of Data -- Chapter 2: Exploratory Data Analysis -- Chapter 3: Unsupervised Learning -- Part II: Inferential Data Analyses -- Chapter 4: Linear Regression -- Chapter 5: Logistic Regression -- Chapter 6: Regularization -- Part III: Machine Learning -- Chapter 7: Support-Vector Machines -- Chapter 8: Deep Learning.

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. Numerous suggestions for further reading guide the reader in finding additional information. This book is for everyone who wants to understand and apply concepts and methods of statistical learning. Typical readers are graduate and advanced undergraduate students in data-intensive fields such as computer science, biology, psychology, business, and engineering, and graduates preparing for their job interviews.

ISBN: 9783031302763

Standard No.: 10.1007/978-3-031-30276-3doiSubjects--Topical Terms:

517247
Statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

A first course in statistical learning = with data examples and Python code /
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520 $a 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. Numerous suggestions for further reading guide the reader in finding additional information. This book is for everyone who wants to understand and apply concepts and methods of statistical learning. Typical readers are graduate and advanced undergraduate students in data-intensive fields such as computer science, biology, psychology, business, and engineering, and graduates preparing for their job interviews.
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