Practical linear algebra for data science: (Record no. 24775)

MARC details
000 -LEADER
fixed length control field 05223cam a2200481 i 4500
001 - CONTROL NUMBER
control field 23231640
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250710113040.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230717s2022 ch a 001 0 eng c
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2022301984
015 ## - NATIONAL BIBLIOGRAPHY NUMBER
National bibliography number GBC2I3176
Source bnb
016 7# - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER
Record control number 020777304
Source Uk
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781098120610
035 ## - SYSTEM CONTROL NUMBER
System control number 23231640
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1348394142
040 ## - CATALOGING SOURCE
Original cataloging agency UKMGB
Language of cataloging eng
Description conventions rda
Transcribing agency UKMGB
Modifying agency BNG
-- BDX
-- OCLCF
-- FIE
-- BNG
-- YT1
-- OCLCO
-- DLC
042 ## - AUTHENTICATION CODE
Authentication code pcc
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA185.D37
Item number COH 2022
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Cohen, Mike X.,
Dates associated with a name 1979-
Relator term author.
245 10 - TITLE STATEMENT
Title Practical linear algebra for data science:
Remainder of title from core concepts to applications using Python /
Statement of responsibility, etc. Mike X. Cohen.
250 ## - EDITION STATEMENT
Edition statement First edition.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. (Place not known):
Name of publisher, distributor, etc. O'REILLY MEDIA,
Date of publication, distribution, etc. 2022.
300 ## - PHYSICAL DESCRIPTION
Extent xiii, 311 pages:
Other physical details illustrations ;
Dimensions 24 cm
500 ## - GENERAL NOTE
General note Includes index.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Cover -- Copyright -- Table of Contents -- Preface -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Introduction -- What Is Linear Algebra and Why Learn It? -- About This Book -- Prerequisites -- Math -- Attitude -- Coding -- Mathematical Proofs Versus Intuition from Coding -- Code, Printed in the Book and Downloadable Online -- Code Exercises -- How to Use This Book (for Teachers and Self Learners) -- Chapter 2. Vectors, Part 1 -- Creating and Visualizing Vectors in NumPy -- Geometry of Vectors
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Operations on Vectors -- Adding Two Vectors -- Geometry of Vector Addition and Subtraction -- Vector-Scalar Multiplication -- Scalar-Vector Addition -- Transpose -- Vector Broadcasting in Python -- Vector Magnitude and Unit Vectors -- The Vector Dot Product -- The Dot Product Is Distributive -- Geometry of the Dot Product -- Other Vector Multiplications -- Hadamard Multiplication -- Outer Product -- Cross and Triple Products -- Orthogonal Vector Decomposition -- Summary -- Code Exercises -- Chapter 3. Vectors, Part 2 -- Vector Sets -- Linear Weighted Combination -- Linear Independence
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note The Math of Linear Independence -- Independence and the Zeros Vector -- Subspace and Span -- Basis -- Definition of Basis -- Summary -- Code Exercises -- Chapter 4. Vector Applications -- Correlation and Cosine Similarity -- Time Series Filtering and Feature Detection -- k-Means Clustering -- Code Exercises -- Correlation Exercises -- Filtering and Feature Detection Exercises -- k-Means Exercises -- Chapter 5. Matrices, Part 1 -- Creating and Visualizing Matrices in NumPy -- Visualizing, Indexing, and Slicing Matrices -- Special Matrices
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Matrix Math: Addition, Scalar Multiplication, Hadamard Multiplication -- Addition and Subtraction -- "Shifting" a Matrix -- Scalar and Hadamard Multiplications -- Standard Matrix Multiplication -- Rules for Matrix Multiplication Validity -- Matrix Multiplication -- Matrix-Vector Multiplication -- Matrix Operations: Transpose -- Dot and Outer Product Notation -- Matrix Operations: LIVE EVIL (Order of Operations) -- Symmetric Matrices -- Creating Symmetric Matrices from Nonsymmetric Matrices -- Summary -- Code Exercises -- Chapter 6. Matrices, Part 2 -- Matrix Norms
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Matrix Trace and Frobenius Norm -- Matrix Spaces (Column, Row, Nulls) -- Column Space -- Row Space -- Null Spaces -- Rank -- Ranks of Special Matrices -- Rank of Added and Multiplied Matrices -- Rank of Shifted Matrices -- Theory and Practice -- Rank Applications -- In the Column Space? -- Linear Independence of a Vector Set -- Determinant -- Computing the Determinant -- Determinant with Linear Dependencies -- The Characteristic Polynomial -- Summary -- Code Exercises -- Chapter 7. Matrix Applications -- Multivariate Data Covariance Matrices
520 ## - SUMMARY, ETC.
Summary, etc. If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. --
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Algebras, Linear
General subdivision Data processing.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Matrices
General subdivision Data processing.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Information visualization
General subdivision Data processing.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Python (Computer program language)
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer programming.
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Alg�ebre lin�eaire
General subdivision Informatique.
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Python (Langage de programmation)
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Algebras, Linear
General subdivision Data processing
Source of heading or term fast
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Python (Computer program language)
Source of heading or term fast
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Books in General collection
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Total Checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Library of Congress Classification     Mzuzu University Library and Learning Resources Centre Mzuzu University Library and Learning Resources Centre 10/07/2025   QA 185.D37 COH 2022 mZUlm-034383 10/07/2025 034383 10/07/2025 Books in General collection

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