A First Course in Statistical Learning
With Data Examples and Python Code
Sorozatcím: Statistics and Computing;
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40 846 Ft
Beszerezhetőség
Becsült beszerzési idő: A Prosperónál jelenleg nincsen raktáron, de a kiadónál igen. Beszerzés kb. 3-5 hét..
A Prosperónál jelenleg nincsen raktáron.
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A termék adatai:
- Kiadás sorszáma 1st ed. 2024
- Kiadó Springer
- Megjelenés dátuma 2025. február 26.
- Kötetek száma 1 pieces, Book
- ISBN 9783031302756
- Kötéstípus Keménykötés
- Terjedelem282 oldal
- Méret 235x155 mm
- Nyelv angol
- Illusztrációk 13 Illustrations, black & white; 714 Illustrations, color 691
Kategóriák
Rövid leírás:
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.
TöbbHosszú leírás:
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.
Több
Tartalomjegyzék:
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.
