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Lieferstatus:	i.d.R. innert 7-14 Tagen versandfertig
Veröffentlichung:	Mai 2026
Genre:	EDV / Informatik
	Artificial Intelligence / Computational Algorithms / data analysis / Data Science / Data Science through Python / Gradient Descent Algorithm / Künstliche Intelligenz / linear regression / machine learning / Maschinelles Lernen / Mathematical introduction / Mathematik für Informatiker / maximum likelihood estimation / Neural Networks / Principal Component Analysis / Probability and Statistics in Computer Science / Programmier- und Skriptsprachen, allgemein / python / Wahrscheinlichkeitsrechnung und Statistik mehr...
ISBN:	9789819536672
EAN-Code:	9789819536672
Verlag:	Springer EN
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D
Seiten:	399
Illustration:	XVII, 399 p. 54 illus., 52 illus. in color. With online files/update., schwarz-weiss Illustrationen, farbige Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

This textbook serves as a companion to "A Mathematical Introduction to Data Science". It uses Python programming to provide a comprehensive foundation in the mathematics needed for data science. It is designed for anyone with a basic mathematical background, including students and self-learners interested in understanding the principles behind the computational algorithms used in data science. The focus of this book is to demonstrate how programming can aid in this understanding and be used in solving mathematical problems. It is written using Python as its programming language, but readers do not need prior knowledge of Python to benefit from it. Some examples from "A Mathematical Introduction to Data Science" are used to illustrate key concepts such as sets, functions, linear algebra, calculus, and probability and statistics, through Python programming, though it is not necessary to have seen the examples before. Further, this textbook shows how those mathematical concepts can be applied in widely used computational algorithms, such as Principal Component Analysis, Singular Value Decomposition, Linear Regression in two and more dimensions, Simple Neural Networks, Maximum Likelihood Estimation, Logistic Regression and Ridge Regression. This textbook is designed with the assumption that readers have no prior knowledge of Python but possess a basic understanding of programming concepts, such as control flow. Ideally, readers should have both this book and its companion, "A Mathematical Introduction to Data Science". However, those with a strong mathematical background and an interest in programming implementations can benefit from reading this textbook alone.