10最好的线性代数教程推荐

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特写 iPhone，显示 Udemy 应用程序和带笔记本的笔记本电脑有数以千计的在线课程和课程可以帮助您提高 线性代数 技能并获得 线性代数 证书。

在这篇博客文章中，我们的专家汇总了 10 个精选列表 最好的 线性代数 课程, 现在在线提供的教程、培训计划、课程和认证。

我们只包括那些符合我们高质量标准的课程。我们花了很多时间和精力来为您收集这些。这些课程适合所有级别的初学者、中级学习者和专家。

以下是这些课程以及它们为您提供的内容！

10最好的线性代数教程推荐

课程名 在校生（人数） 评论（计数）
1. Become a Linear Algebra Master 我们的最佳选择 26732+ 3707+
2. Complete linear algebra: theory and implementation in code 25828+ 3821+
3. Learn Algebra The Easy Way! 6447+ 1380+
4. Master Linear Algebra 2020: The Complete Study Of Spaces 6015+ 382+
5. College Level Advanced Linear Algebra! Theory & Programming! 5937+ 376+
6. Linear Algebra for Beginners: Open Doors to Great Careers 4614+ 495+
7. Complete Linear Algebra for Data Science & Machine Learning 4074+ 652+
8. Linear Algebra Mathematics for Machine Learning Data Science 3937+ 274+
9. “PCA & multivariate signal processing, applied to neural data” 3920+ 369+
10. Linear Algebra and Geometry 1 3042+ 383+

1. Become a Linear Algebra Master 经过 Krista King Udemy课程 我们的最佳选择

“Learn everything from Linear Algebra, then test your knowledge with 400+ practice questions”

截至目前，超过 26732+ 人们已经注册了这门课程，而且已经结束了 3707+ 评论.

课程内容
Getting started
Operations on one matrix
Operations on two matrices
Matrices as vectors
Dot products and cross products
Matrix-vector products
Transformations
Inverses
Determinants
Transposes
Orthogonality and change of basis
Orthonormal bases and Gram-Schmidt
Eigenvalues and Eigenvectors
Final exam and wrap-up

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2. Complete linear algebra: theory and implementation in code 经过 Mike X Cohen Udemy课程

“Learn concepts in linear algebra and matrix analysis, and implement them in MATLAB and Python.”

截至目前，超过 25828+ 人们已经注册了这门课程，而且已经结束了 3821+ 评论.

课程内容
Introductions
Get the course materials
Vectors
Introduction to matrices
Matrix multiplications
Matrix rank
Matrix spaces
Solving systems of equations
Matrix determinant
Matrix inverse
Projections and orthogonalization
Least-squares for model-fitting in statistics
Eigendecomposition
Singular value decomposition
Quadratic form and definiteness
Bonus section

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3. Learn Algebra The Easy Way! 经过 Organic Chemistry Tutor Udemy课程

“Algebra Review – Slope, Graphing Linear Equations, Exponents, Factoring, Solving Quadratic Equations, & Radicals”

截至目前，超过 6447+ 人们已经注册了这门课程，而且已经结束了 1380+ 评论.

课程内容
“Introduction
Basic Arithmetic – Addition, Subtraction, Multiplication, and Division
Fractions Review Tutorial
Solving Linear Equations
Order of Operations
Graphing Linear Equations
Linear Inequalities and Absolute Value Expressions
Polynomials
Factoring
Systems of Linear Equations
Quadratic Equations
Rational Expressions
Radical Expressions
Complex Imaginary Numbers
Exponential and Logarithmic Functions
Functions
Conic Sections
Arithmetic & Geometric Sequences”

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4. Master Linear Algebra 2020: The Complete Study Of Spaces 经过 Kody Amour Udemy课程

Learn How to Define Space And How it is Characterized And Measured. We Make Linear Algebra Math Fun And Easy.

截至目前，超过 6015+ 人们已经注册了这门课程，而且已经结束了 382+ 评论.

课程内容
“Introduction Video
Introduction
Matrices and Their Properties
Vector Spaces and Linear Transformations
Orthogonality, Norms and Inner Product Spaces
Eigenvalues
Conclusion”

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5. College Level Advanced Linear Algebra! Theory & Programming! 经过 “Ahmed Fathy, MSc” Udemy课程

“Linear Algebra (matlab – python) & Matrix Calculus For Machine Learning, Robotics, Computer Graphics, Control, & more !”

截至目前，超过 5937+ 人们已经注册了这门课程，而且已经结束了 376+ 评论.

课程内容
“Introduction To The Course
Introduction To Matrices : Linear Independence And Matrix Multiplication
Introduction To Gaussian Elimination And Matrix Inverse
Test Your Self ! – Exam 1 !
The Computer Graphics Section !
The Robotics Section !
Test Your Self ! – Exam 2 !
Test Your Self ! – Exam 3 !
EigenValues & EigenVectors ( I ) : Introduction
EigenValues & EigenVectors ( II ) : Difference Equations
EigenValues & EigenVectors ( III ) : Differential Equations
Test Your Self ! – Exam 4 !
Matrix Inverse Using Cofactors & The Cayley Hamilton Theorem
Back To Systems Of Linear Equations ! – The Matrix Rank
The Four Sub-spaces Of A Matrix
Solving The Unsolvable : Linear Regression, Projection Matrix & Normal Equation
Test Your Self ! – Exam 5 !
A Section On Symmetric Matrices
A Section On Machine Learning And DataScience
Appendix A – The Lagrange Multipliers
The Principal Component Analysis (PCA)
Test Your Self ! – Exam 6 !
The Singular Value Decomposition (SVD)
The Pseudo Inverse Of A Matrix
Test Your Self ! – Exam 7 !
The LU Decomposition
A Video On Positive Definite Matrices – Will Come Back In A Subsequent Section !
Appendix B : The Taylor Expansion
Back To Positive Definite Matrices !
Determinants !
Matrix Calculus – I : The Basics
Matrix Calculus II : On The Relation Between The Jacobian And Double Integrals
Matrix Calculus – III : More On Matrix Calculus
Test Your Self ! – The Final Exam !
EXTRA – I :: Homogeneous Coordinates And The Projection Matrix Derivation !
BONUS : Get My Other Courses !”

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6. Linear Algebra for Beginners: Open Doors to Great Careers 经过 Richard Han Udemy课程

“Learn the core topics of Linear Algebra to open doors to Computer Science, Data Science, Actuarial Science, and more!”

截至目前，超过 4614+ 人们已经注册了这门课程，而且已经结束了 495+ 评论.

课程内容
Introduction
Solving Systems of Linear Equations
Vectors
Matrix Operations
Properties of Matrix Addition and Scalar Multiplication
The Inverse of a Matrix
Determinants
Properties of Determinants
Vector Spaces
Subspaces
Span and Linear Independence
Basis and Dimension
Concluding Letter

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7. Complete Linear Algebra for Data Science & Machine Learning 经过 “Kashif A., Abdullah A.” Udemy课程

“Linear Algebra for Data Science, Big Data, Machine Learning, Engineering & Computer Science. Master Linear Algebra”

截至目前，超过 4074+ 人们已经注册了这门课程，而且已经结束了 652+ 评论.

课程内容
Welcome and Introduction
Basics of Matrices
Basics of Matrices (Continued)
Matrices and Systems of Linear Equations
Matrix Algebra and Operations
Determinant of a Matrix
Inverse of a Matrix
Properties of Determinants
*** OPTIONAL: Introduction to Vectors
Vector Spaces
Subspace and Nullspace
Span and Spanning Sets
Linear Dependence and Independence
Basis and Dimension
Eigenvalues and Eigenvectors
Basic Algebra Concepts (Additional Lessons)
Congratulations and Bonus Material

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8. Linear Algebra Mathematics for Machine Learning Data Science 经过 Manifold AI Learning ® Udemy课程

“Go Zero to Pro – Complete linear algebra – Mathematics for data science, machine learning & Deep Learning”

截至目前，超过 3937+ 人们已经注册了这门课程，而且已经结束了 274+ 评论.

课程内容
Vectors Basics
Vector Projections
Basis of Vectors
Matrix Basics from High school
Matrices – Setting up the stage – Transformations
Gaussian Elimination
Einstein Summation convention – Non Orthogonal basis – Gram Schmidt Process
Eigen Problems
Google Pagerank Algorithm
SVD – Singular Value Decomposition
Pseudo Inverse
Matrix Decompositions
Solving the Linear Regression using Matrix Decomposition methods
Linear Regression from Scratch
Linear Algebra in Natural Language Processing
Linear Algebra for Deep Learning – Getting started with Pytorch
Linear Regression Using Pytorch
Python Basics
Python for Data Science
Basics of Statistics
Appendix : Python for Data Science

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9. “PCA & multivariate signal processing, applied to neural data” 经过 Mike X Cohen Udemy课程

Learn and apply cutting-edge data analysis techniques for “big neurodata” (theory and MATLAB/Python code)

截至目前，超过 3920+ 人们已经注册了这门课程，而且已经结束了 369+ 评论.

课程内容
Introduction
Download all course materials
Dimensions and sources
Linear algebra crash course
Creating and interpreting covariance matrices
Dimension reduction with PCA
Source separation with GED
Source separation for steady-state responses
Independent components analysis (ICA)
Overfitting and inferential statistics
Big questions in multivariate neuroscience
Bonus section

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10. Linear Algebra and Geometry 1 经过 “Hania Uscka-Wehlou, Martin Wehlou” Udemy课程

“Systems of equations, matrices, vectors, and geometry”

截至目前，超过 3042+ 人们已经注册了这门课程，而且已经结束了 383+ 评论.

课程内容
“Introduction to the course
Some basic concepts
Systems of linear equations; building up your geometrical intuition
Solving systems of linear equations; Gaussian elimination
Some applications in mathematics and natural sciences
Matrices and matrix operations
Inverses; Algebraic properties of matrices
Elementary matrices and a method for finding A inverse
Linear systems and matrices
Determinants
Vectors in 2-space, 3-space, and n-space
Distance and norm in R^n
Dot product, orthogonality, and orthogonal projections
Cross product, parallelograms and parallelepipeds
Lines in R^2
Planes in R^3
Lines in R^3
Geometry of linear systems; incidence between lines and planes
Distance between points, lines, and planes
Some words about the next course
Extras”

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