"This post includes affiliate links for which I may make a small commission at no extra cost to you should you make a purchase."

The **10 Best Linear Algebra Books **list have been recommended not only by normal readers but also by experts.

You’ll also find that these are top-ranking books on the US Amazon Best Sellers book list for the **Linear Algebra category of books**.

If any of the titles interest you, I’d recommend checking them out by clicking the “Check Price” button. It’ll take you to the authorized retailer website, where you’ll be able to see reviews and buy it.

Let’s take a look at the list of **10 Best Linear Algebra Books**.

## 10 Best Linear Algebra Books

Now, let’s dive right into the list of **10 Best Linear Algebra Books**, where we’ll provide a quick outline for each book.

### 1. Algebra Part 1 (Quickstudy Reference Guides – Academic) by Inc. BarCharts Review Summary

### 2. Graph Paper Composition Notebook: Grid Paper Notebook, Quad Ruled, 100 Sheets (Large, 8.5 x 11) (Graph Paper Notebooks) by Joyful Journals Review Summary

Graph Paper Composition Notebook: Grid Paper Notebook, Quad Ruled, 100 Sheets (Large, 8.5 x 11) (Graph Paper Notebooks)
Graph Paper Composition Notebook, Journal, Diary â€¢ One Subject â€¢ 100 Pages Makes a wonderful daily graph/grid notebook to draw, write, journal, take notes, make lists, and much more creativity! This is a simple and durable all-purpose daily graph/grid notebook. There is plenty of room inside for drawing, writing notes, journaling, doodling, list making, creative writing, school notes, and capturing ideas. It can be used as a notebook, journal, diary, or composition book. This paperback notebook is 8.5 x 11 in / 21.59 x 27.94 cm with a soft, matte cover and has 100 quad ruled pages. Perfect for all ages — kids or adults! Wonderful as a gift, present, or personal notebook! About this notebook: * 100 graph ruled pages * Grid ruled on both sides with thin gray lines * Perfect for architects, artists, and any drawing activities * High-quality matte cover for a professional finish * Perfect size at 8.5 x 11 in / 21.59 x 27.94 cm — Larger than most Scroll up and click ‘buy’ to grab one today!

### 3. Quantum Mechanics: The Theoretical Minimum by Leonard Susskind Review Summary

Sale
Quantum Mechanics: The Theoretical Minimum
First he taught you classical mechanics. Now, physicist Leonard Susskind has teamed up with data engineer Art Friedman to present the theory and associated mathematics of the strange world of quantum mechanics. In this follow-up to the New York Times best-selling The Theoretical Minimum , Susskind and Friedman provide a lively introduction to this famously difficult field, which attempts to understand the behavior of sub-atomic objects through mathematical abstractions. Unlike other popularizations that shy away from quantum mechanics’ weirdness, Quantum Mechanics embraces the utter strangeness of quantum logic. The authors offer crystal-clear explanations of the principles of quantum states, uncertainty and time dependence, entanglement, and particle and wave states, among other topics, and each chapter includes exercises to ensure mastery of each area. Like The Theoretical Minimum , this volume runs parallel to Susskind’s eponymous Stanford University-hosted continuing education course. An approachable yet rigorous introduction to a famously difficult topic, Quantum Mechanics provides a tool kit for amateur scientists to learn physics at their own pace.

### 4. Linear Algebra Done Right (Undergraduate Texts in Mathematics) by Sheldon Axler Review Summary

Sale
Linear Algebra Done Right (Undergraduate Texts in Mathematics)
This best-selling textbook for a second course in linear algebra is aimed at undergrad math majors and graduate students. The novel approach taken here banishes determinants to the end of the book. The text focuses on the central goal of linear algebra: understanding the structure of linear operators on finite-dimensional vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. The third edition contains major improvements and revisions throughout the book. More than 300 new exercises have been added since the previous edition. Many new examples have been added to illustrate the key ideas of linear algebra. New topics covered in the book include product spaces, quotient spaces, and dual spaces. Beautiful new formatting creates pages with an unusually pleasant appearance in both print and electronic versions. No prerequisites are assumed other than the usual demand for suitable mathematical maturity. Thus the text starts by discussing vector spaces, linear independence, span, basis, and dimension. The book then deals with linear maps, eigenvalues, and eigenvectors. Inner-product spaces are introduced, leading to the finite-dimensional spectral theorem and its consequences. Generalized eigenvectors are then used to provide insight into the structure of a linear operator.

### 5. Algebra, Part 2 (Quick Study) by S. B. Kizlik Review Summary

Algebra, Part 2 (Quick Study)
Algebra 2 is the advanced QuickStudy guide specially designed for students who are already familiar with Algebra 1.

### 6. Special Relativity and Classical Field Theory: The Theoretical Minimum by Leonard Susskind Review Summary

Sale
Special Relativity and Classical Field Theory: The Theoretical Minimum
The third volume in the bestselling physics series cracks open Einstein’s special relativity and field theory Physicist Leonard Susskind and data engineer Art Friedman are back. This time, they introduce readers to Einstein’s special relativity and Maxwell’s classical field theory. Using their typical brand of real math, enlightening drawings, and humor, Susskind and Friedman walk us through the complexities of waves, forces, and particles by exploring special relativity and electromagnetism. It’s a must-read for both devotees of the series and any armchair physicist who wants to improve their knowledge of physics’ deepest truths.

### 7. Quantum Physics for Beginners: Discover the Science of Quantum Mechanics and Learn the Basic Concepts from Interference to Entanglement by Analyzing the Most Famous Quantum Experiments by Daniel Golding Review Summary

Quantum Physics for Beginners: Discover the Science of Quantum Mechanics and Learn the Basic Concepts from Interference to Entanglement by Analyzing the Most Famous Quantum Experiments
### â˜… B&W Edition â˜… Is it possible for two objects to be in two places at once? Can cause and effect happen in reverse? Is time travel possible? Believe it or not, it is possible: welcome to the Quantum World! Unlike other arguments, however, the real difficulty is not in understanding, but in accepting something completely senseless, precisely in the right meaning of the term: not sensible, that is, contrary to the perception of our senses. You will notice that quantum mechanics is much “easier” than the theory of relativity. In fact, you could get a child to help you digest certain concepts. The great difficulty does not lie in their complexity, but their absurdity in terms of logic acquired after many years of existence in a world that constantly follows certain rules. The more the brain is free of preconceptions and ingrained notions, the better it is. You will learn: * What is interference ; * The Double-Slit Experiment; * Quantum wave function; * Feynman’s works ; * The relation between waves and particles; * The Heisenberg Uncertainty Principle; * How particles can be in multiple places at once; * Quantum entanglement ; * The Schrodinger’s cat thought experiment; * The Copenhagen interpretation and need for an observer; â€¦and much more! Quantum Physics is at the basis of all the technological innovations of today, from atomic energy to computer microelectronics, from digital clocks to lasers, semiconductor systems, photoelectric cells, diagnostic and treatment equipment for many diseases. In short, today we can live in a “modern” way thanks to Quantum Physics and its applications. This comprehensive beginner’s guide to quantum mechanics explains the most important and stunning quantum experiments that show quantum physics is real. Are you ready? Let’s dive deep into the fascinating science of Quantum Physics. Happy reading!

### 8. Introduction to Linear Algebra (Gilbert Strang) by Gilbert Strang Review Summary

Sale
Introduction to Linear Algebra (Gilbert Strang)
Gilbert Strang’s textbooks have changed the entire approach to learning linear algebra — away from abstract vector spaces to specific examples of the four fundamental subspaces: the column space and nullspace of A and A’. This new fifth edition has become more than a textbook for the basic linear algebra course. That is its first purpose and always will be. The new chapters about applications of the SVD, probability and statistics, and Principal Component Analysis in finance and genetics, make it also a textbook for a second course, plus a resource at work. Linear algebra has become central in modern applied mathematics. This book supports the value of understanding linear algebra. Introduction to Linear Algebra, Fifth Edition includes challenge problems to complement the review problems that have been highly praised in previous editions. The basic course is followed by eight applications: differential equations in engineering, graphs and networks, statistics, Fourier methods and the FFT, linear programming, computer graphics, cryptography, Principal Component Analysis, and singular values. Audience: Thousands of teachers in colleges and universities and now high schools are using this book, which truly explains this crucial subject. This text is for readers everywhere, with support from the websites and video lectures. Every chapter begins with a summary for efficient review. Contents: Chap. 1: Introduction to Vectors; Chap. 2: Solving Linear Equations; Chap. 3: Vector Spaces and Subspaces; Chap. 4: Orthogonality; Chap. 5: Determinants; Chap. 6: Eigenvalues and Eigenvectors; Chap. 7: Singular Value Decomposition; Chap. 8: Linear Transformations; Chap. 9: Complex Vectors and Matrices; Chap. 10: Applications; Chap. 11: Numerical Linear Algebra; Chap. 12: Linear Algebra in Probability and Statistics; Matrix Factorizations; Index; Six Great Theorems.

### 9. Dotted Paper 8.5 X 11: Dotted Notebook Paper Letter Size | Bullet Dot Grid Graphing Pad Journal With Page Numbers For Drawing & Note Taking (Dot Graph Paper Notebook) by M.J. Brixey Review Summary

Dotted Paper 8.5 X 11: Dotted Notebook Paper Letter Size | Bullet Dot Grid Graphing Pad Journal With Page Numbers For Drawing & Note Taking (Dot Graph Paper Notebook)
This Dotted Paper 8.5 X 11 Notebook contains dot grid paper with 110 pages, measuring at 8.5 x 11 inch (21.59cm x 27.94cm). White paper quality is good for writing, drawing, sketching, journaling, take notes, etc with pen or pencil in a daily used. dot graph have each spaced 5mm (1/5 inch) with thin and light gray color so you can easily to write, draw in correct shape with dot. Soft cover (Matte finish). This notebook is perfect for people who want to create to do lists, set goals and habit tracking, plan things, be organized, doodle, bullet journaling, calligraphy, hand lettering and be creative in any possible way. You can cut it out to use as loose leaf paper. Details: * Total 110 pages * Bound Notebook – Non-perforated * Paper size 8.5 X 11 Inch (21.59cm X 27.94cm) * White paper with dotted graph, Each spaced 5mm (1/5 Inch) * Soft cover with glossy finish * Numbered pages

### 10. Linear Algebra and Optimization for Machine Learning: A Textbook by Charu C. Aggarwal Review Summary

Sale
Linear Algebra and Optimization for Machine Learning: A Textbook
This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.