5 Best Linear Algebra Courses to Learn in 2024
My favorite online courses to learn Linear Algebra in depth
Hello guys, if you want to learn Linear Algebra in depth and looking for best resource slike tutorials and online courses then you have come to the right place. In the past, I have shared best Maths courses and best Statistics courses and in this article, I am going to share best online courses to learn Linear Algebra in depth .
Linear algebra serves as a foundational pillar of mathematics and plays a crucial role in various fields, including computer science, physics, engineering, Machine LEarning and data analysis.
Its applications are vast, ranging from understanding complex systems to solving optimization problems.
Whether you’re a student, a professional looking to enhance your skills, or simply an avid learner intrigued by the beauty of mathematical concepts, finding the best linear algebra course can be a transformative step towards mastering this subject in depth.
In this article, I will guide you through the top linear algebra online courses, designed to provide comprehensive and immersive learning experiences.
We will explore various online platforms and educational resources that offer exceptional courses taught by renowned instructors. Whether you prefer self-paced learning or interactive classroom settings, there is a course that caters to your preferred learning style.
Throughout our exploration, we will consider factors such as course content, teaching methodologies, practical applications, and student reviews to help you make an informed decision.
What is Linear Algebra? Why Data Scientists Should Learn it?
Linear algebra is a branch of mathematics that focuses on vector spaces and linear mappings between these spaces. It provides a framework for representing and solving systems of linear equations, which are foundational in various fields of science and engineering.
In the context of data science and machine learning, learning linear algebra is crucial for several reasons:
- Representation of Data
Linear algebra provides the mathematical foundation for representing and manipulating data in a structured way. Data, especially in the form of tables or matrices, can be efficiently handled using linear algebra operations. - Linear Transformations
Many machine learning algorithms involve transformations of data. Linear algebra helps in understanding and implementing these transformations efficiently. Linear transformations, expressed through matrices, are fundamental in tasks like dimensionality reduction and feature engineering. - Matrix Operations
Matrices are central to linear algebra, and many machine learning algorithms can be expressed and implemented using matrix operations. Operations like matrix multiplication, inversion, and decomposition are used extensively in tasks such as solving linear systems, optimization, and eigenvalue problems. - Machine Learning Models
Linear algebra is deeply embedded in the foundations of machine learning models. Linear regression, for example, relies on the principles of linear algebra to find the best-fitting line to a set of data points. Support Vector Machines, Principal Component Analysis, and neural networks are other examples where linear algebra is integral to understanding and implementing these models. - Optimization
Many machine learning algorithms involve optimization problems, where the goal is to find the parameters that minimize or maximize a certain objective function. Linear algebra is essential in formulating and solving these optimization problems efficiently. - Eigenvalues and Eigenvectors
Eigenvalues and eigenvectors are concepts from linear algebra that are frequently used in machine learning. They have applications in dimensionality reduction techniques like PCA (Principal Component Analysis) and spectral clustering - Deep Learning
In deep learning, neural networks are often represented and trained using linear algebra concepts. Operations such as forward and backward propagation in neural networks heavily rely on linear algebra computations.
In summary, learning linear algebra is crucial for data scientists and machine learning engineers because it provides the mathematical tools necessary for understanding, implementing, and optimizing machine learning algorithms.
It forms the backbone of many concepts and techniques used in data science and machine learning and enhances the ability to work with data in a more efficient and meaningful way.
What are the best Linear Algebra courses you can join in 2024
Here are the 5 best online courses to learn Linear Algebra in depth:
1. Mathematics for Machine Learning: linear algebra (Coursera)
2. Become a linear algebra master(Udemy)
3. Learn linear algebra (khan academy)
4. Linear algebra by MIT( OCW MIT)
5. Coding the matrix by Philip Klein
Let’s see each one in detail.
1. Mathematics for Machine Learning: linear algebra (Coursera)
This is one of the best Mathematics course on Coursera, provided by Imperial College of London. In this course on Linear Algebra, you will learn what is linear algebra is and its relation to vectors and matrices.
Then you will use this knowledge of matrix and vector to solve the problems of linear algebra. You will also be implementing some of these ideas not only in pen or paper but also on code,.
You’ll write code blocks and encounter Jupyter notebooks in Python at the end of the course, but don’t worry, these will be quite short, focussed on the concepts.
This course is also part of Mathematics for Machine Learning and Data Science Specialization on Coursera, which is a great resource to master Maths and Statistics for Data Science.
Content of course –
- There are four modules in this course named
- Introduction to linear algebra and mathematics.
- Vectors
- Matrices In linear algebra
- Matrices make linear mapping
Benefits-
- This course will also provide the certificate after completion.
- 50 video lectures for mastering the course.
- Lifetime access.
Here is the link to join this course — Mathematics for Machine Learning: linear algebra
By the way, If you are planning to join multiple Coursera courses or specializations, then consider taking a Coursera Plus subscription which provides you unlimited access to their most popular courses, specialization, professional certificate, and guided projects. It costs around $399/ per annum but is worth it because you get access to more than 7000+ courses and projects, and you can also get unlimited certificates.
2. Become a Linear Algebra Master (Udemy)
This is another great course to learn about Linear Algebra online. This Udemy course is for anyone who is comfortable with the Fundamentals of Math, like arithmetic (addition, subtraction, multiplication, division) of positive and negative numbers, fractions, and decimals.
This is a course full of 247 lectures with 69 quizzes.
Content of this course-
- Solving linear systems and Gauss-Jordan elimination are examples of operations on a single matrix.
- Matrix multiplication and elimination matrices are examples of operations on two matrices.
- Cauchy-Schwarz and vector triangle inequalities, as well as dot and cross product.
- Eigenvalues and Eigenvectors, encompassing eigenvalues, eigenvectors, and eigenspaces.
Benefits-
- certificate on completion of course.
- Self-paced
- Full-time access
- 15-hours on-demand video.
Talking about social proof, more than 34,523 students have joined this course and it has on average 4.7 rating which is great.
Here is the link to join this course — Become a Linear Algebra Master
3. Learn linear algebra (khan academy) (Free)
If you wanted to learn basics and want to have strong fundamentals this is a perfect course for you. This free Linear Algebra course is designed by skilled professors and industry specialists who have enough knowledge of linear algebra to turn you into a professional.
You can enroll in other advanced courses after you complete this course.
Content of the course –
- Vectors and spaces — vectors, subspaces and basis of subspaces, cross and dot product of vectors.
- Alternate coordinate system — orthogonal bases and Gram-Schmidt process, orthogonal projections.
- Matrix transformation- functions and linear transformation and inverse of it, transformation and multiplication, the question on inverses and determinants
Benefits –
- This is a free course that focuses on providing a thorough understanding of linear algebra topics.
- Learn how to define and visualize a vector, as well as some fundamental operations such as addition, subtraction, and scalar multiplication.
- Includes a variety of video lectures, quizzes, practice examinations, and real-life examples to ensure you get the most up-to-date information.
4. Linear algebra by MIT( OCW MIT) (Free)
What’s better than learning from one of the top Universities in the world MIT.
A top professor from teaching linear algebra in this course. Graphs and Networks, Systems of Differential Equations, Least Squares and Projections, and Fourier Series and the Fast Fourier Transform are the four
primary applications in the book by professor Gilbert Strang that the course highlights.
Course content-
- Elimination (pivots, multipliers, back substitution, invertibility of A, factorization into A = LU) is used to solve Ax = b for square systems.
- Ax = b (column space containing b, the rank of A, null space of A, and special solutions to Ax = 0 from row reduced R) is a complete solution.
- Solutions based on least squares (closest line by understanding projections)
- Gram-Schmidt orthogonalization (factorization into A = QR) Determinant properties (which lead to the cofactor formula and the sum over all n! permutations, as well as applications to inv(A) and volume).
- To solve difference and differential equations, eigenvalues and eigenvectors (diagonalizing A, calculating powers Ak, and matrix exponentials) are used.
- Real eigenvalues and orthogonal eigenvectors, tests for x’Ax > 0, applications)
- Symmetric matrices and positive definite matrices (real eigenvalues and orthogonal eigenvectors, tests for x’Ax > 0, applications)
- Changes in basis and linear transformations (connected to the Singular Value Decomposition — orthonormal bases that diagonalize A).
Benefits-
- Excessive study materials, assignments, and 40-hours detailed videos.
- Quarterly examination to test your learnings in this course.
5. Coding the matrix by Philip Klein (Free)
Machine learning is based on a set of clever mathematical and logical theories and methods. This the course begins with a discussion of the limits of eigenvalue analysis before delving into the details of singular value decomposition and other matrix operations.
Content of the course-
- Law of large numbers
- The geometry of high dimensions
- Random walks in Euclidean space
- Gradient Descent methods
- Graph partitioning
- Bayesian or belief networks
Benefits-
- Self-paced
- Total having 20 hours of advanced level mathematics.
6. Complete linear algebra: theory and implementation in code
This is another Linear Algebra course from Udemy which you can join to learn Linear Algebra concepts and matrix analysis, and implement them in MATLAB and Python.
This course is created by Mike X Cohen, an expert in this field and it will teach you all necessary linear algebra concepts. You will also learn the The math behind most of AI (artificial intelligence).
The course also provides additional insights into solving problems in linear algebra, including homeworks and applications.
Talking about social proof, more than 30,917 students have joined this course and it has on average 4.7 rating from close to 4000 participants which is great.
Here is the link to join this course — Complete linear algebra: theory and implementation in code
That’s all about the best online courses to learn Linear Algebra online in 2024. In conclusion, mastering linear algebra is an essential foundation for success in various fields, particularly in data science and machine learning and these courses will help you master Linear algebra for Data Science and Machine Learning.
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Thanks for reading this article so far. If you find these best Linear Algebra courses useful in 2024 then please share them with your friends and colleagues. If you have any questions or feedback then please drop a note.
P. S. — If you want to maser Mathematics for Data Science and Machine Learning and need a single course which cover all the bases then there i sno better resource then Mathematics for Machine Learning and Data Science Specialization on Coursera by Imperial College of London. Tailored specifically for those interested in applying linear algebra to machine learning, this course provides a solid theoretical understanding along with practical applications.
