5 best courses for Linear algebra
Linear algebra is a fundamental subject in mathematics with applications in fields ranging from computer graphics to physics. It is a great idea to take a linear algebra course if you are a student, engineer, or working professional looking to improve your skills. There are a lot of course options available, making it difficult to choose one. Throughout this blog post, we will explore five of the best linear algebra courses. Whether you’re a beginner or an experienced learner, you’ll find something here. Let’s begin!
Benefits of learning linear algebra
1. You will be able to analyze data using least-squares, regression, and multivariate methods.
2. Enhance the quality of mathematical simulations in engineering, computational biology, finance, and physics.
3. Learn about data compression and dimension reduction (PCA, SVD, eigendecomposition) and how to apply them.
4. Become familiar with the math that underpins machine learning and linear classification algorithms.
5. A deeper understanding of the principles of signal processing methods, particularly the use of filtering and multivariate subspaces.
6. Learn how linear algebra, matrices, and geometry are related and how they can be used together.
7. It is important to gain more experience in Python and MATLAB in order to gain a better understanding of math concepts and machine learning concepts.
8. There is no doubt that linear algebra is a prerequisite to machine learning and artificial intelligence (AI).
Top 5 best courses for Linear Algebra
Number 1. Complete linear algebra: theory and implementation in code
Complete linear Algebra: theory and Implementation in Code course will teach you such thing
1. Develop an understanding of linear algebraic concepts, and be able to provide proofs for them.
2. Using scientific programming languages (MATLAB, Python) to implement linear algebra concepts.
3. The application of linear algebra concepts to real-world datasets.
4. Make sure you pass your linear algebra exam with flying colors!.
5. Using linear algebra on a computer with confidence is possible.
6. This course will provide you with insight into how to solve problems in linear algebra, including homework and applications.
7. You will be able to learn advanced linear algebra topics with confidence.
8. Take a closer look at some of the important maths that underlie machine learning.
9. A great deal of AI (artificial intelligence) is based on math.
Who this algebra course is for:
If you are interested in learning more about matrices and vectors, this is the course for you.
This course is designed for students who are looking for supplemental instruction/practice for a linear algebra course.
It is designed for engineers who wish to refresh their knowledge of matrices and decompositions through this course.
Computational biologists who are interested in learning more about the mathematics behind the field of biology.
There is a lot of linear algebra in data science (linear algebra is everywhere in data science!).
Researchers studying statistics.
A person who would like to learn more about the important math that underpins machine learning.
A person who has studied theoretical linear algebra and would like to implement concepts into a computer program.
There are many types of computational scientists in the field (statistics, biology, engineering, neuroscience, psychology, physics, etc.).
This is the perfect opportunity for someone who wants to learn about eigendecomposition, diagonalization, and singular value decomposition!
Students who are studying artificial intelligence
Top reviewed from algebra course buyer
1- There is no doubt that this is the best linear algebra course on Udemy. Not only do you learn the theory, but you also learn how to apply it in the real world. It is very helpful to master abstract linear algebra concepts by using code. As an example, Mike spends hours illustrating some concepts, such as the eigendecomposition and the SVD, with many different aspects. This course is an excellent choice if you are interested in linear algebra and would like to learn more about it.
2-The overall course is good, there is a lot of discussion in the course about linear algebra concepts.I think it depends on who learns this course, some people might not like it, because he looks forward to some concepts in each section, such as discussing singular values, PCA, SVT, and spectral theory several times before their introductions. Some people might not prefer it, because it feels too intense when his brain has a lot of linear computations to do.I would like to ask the guys on this course to review some code and maybe even code for themselves. It helps a lot to understand some details because of this. In some cases, we may think that we fully understand something, when in fact we do not.
3- The third course I have taken from Mike and perhaps one of my favorites. Mike is an excellent teacher who clearly explains these complex topics in a clear and understandable manner. As well as being a great answer to any questions you may have, Mike is also a great resource for you. It would be my personal recommendation to anyone who wishes to gain a deeper understanding of many of the algorithms and mathematical processes that are fundamental to statistics and machine learning to take this course.
Number 2. Learn Algebra The Easy Way!
What you’ll learn from this ALGEBRA COURSE
Students will be able to gain a deep understanding of the fundamentals and principles of algebra after taking This course.
The course is designed for university students taking intermediate algebra and college algebra as well as high school students who are taking algebra 1, algebra 2, or algebra 3.
Here is a list of topics covered in this course:
1. The Basics of Arithmetic – Addition, Subtraction, Multiplication, and Division
2. Fractions Review – In this lesson, you will learn how to add, subtract, multiply, and divide fractions.
3. Solving Linear Equations – This module covers single-step equations as well as multi-step equations with variables and parentheses on both sides.
4. There is an order of operations that includes PEMDAS as well as evaluating mathematical expressions.
5. Defining Absolute Value Functions and Inequalities
6. Linear Equations Graphed Using The Slope & The Y-Intercept
7. Polynomials – The addition, subtraction, multiplication, and division of polynomials.
8. Factoring – Differences between perfect squares, trinomials, sums, and differences between perfect cubes and other functions
9. Systems of Linear Equations – There are examples that include two variables and three variables as well as word problems.
10. Quadratic Equations – The Quadratic Formula, Completing the Square, Graphing Quadratic Equations, and Word Problems related to Quadratic Equations.
11. Rational Expressions – Solving Rational Equations, Adding, Subtracting, Multiplying, Dividing, and Graphing Rational Expressions.
12. Radical Expressions – The purpose of this section is to show how radical expressions can be simplified and how radical equations can be solved.
13. Complex Imaginary Numbers – Solving Equations, Graphing, and Performing Basic Operations on These Numbers
14. The Logarithmic and Exponential Functions
15. Functions – Domain and Range, Evaluating Functions, Horizontal Line Tests, and Vertical Line Tests
16. Conic Sections – These include circles, ellipses, hyperbolas, and parabolas
17. Mathematical and geometric sequences based on arithmetic
Who this algebra course is for:
Students in high school and college, including adults are returning to college.
Top Reviewed from the Algebra course buyer
1- This is helping me to work through the areas of pre-algebra that my college teacher hasn’t explained clearly enough so that I am able to understand it in class I am studying pre-algebra in college. With this course and my class studies, I am able to make the connection between x and y so that I will be able to better understand it.
2- It is pleasing to the ear to hear the instructor’s voice and speech patterns and it is clear and concise for the brain to understand. As Julio breaks the problems down to their most basic level without being tedious or boring, he gets right to the point. It is understandable that everyone learns in a different way. I personally prefer the Organic Chemistry Tutor (Julio) to any other.
3- It was exactly what I was looking for. I would like to refresh your memory on Algebra. In grade school, I was supposed to learn this stuff, but my class was so rambunctious that I worried that I had missed my chance to learn it. Thank you for giving us a second chance to prove ourselves.
Number 3. Linear Algebra for Data Science & Machine Learning A-Z 2023
The Fundamentals of Linear Algebra as well as tips and tricks for acing your Linear Algebra exams
An overview of the basics of matrices, including the notation, dimensions, types, addresses of the entries, etc.
Various operations can be performed on a single matrix, such as scalar multiplication, transposition, determinant and adjoint operations.
There are a number of operations one can perform on two matrices, including addition, subtraction, and multiplication of them.
Performing elementary row operations on a row and finding the Echelon Forms (REF and RREF).
The Cofactor method and inverse matrices, including invertible and singular matrix inverses.
A system of linear equations can be solved using matrices and inverse matrices, including Cramer’s rule in order to solve the equation AX = B.
How to perform Gauss-Jordan elimination based on properties of determinants.
The addition and subtraction of matrices as vectors, as well as the Head-to-Tail rule, components, magnitude, and midpoint of vectors, are covered here.
A vector space consists of dimensions as well as Euclidean spaces, properties of closure, and axioms.
There are linear combinations as well as spans in vector spaces, as well as linear dependence among vector spaces.
The null space and subspace of a matrix, as well as the products of a matrix and a vector.
A vector space is created based on a set of given vectors and it is checked if a set of those vectors forms the basis of that vector space.
How to find Eigenvalues and Eigenvectors of a function, as well as how to find corresponding Eigenvalues and Eigenvectors.
Who this course is for:
Those students who are enrolled or planning to enroll in a Linear Algebra class and who want to excel in it
Those who need a refresher on their math skills, especially algebra and linear algebra, will benefit from this course
Those who are interested in working with linear systems and vector spaces such as engineers, scientists, and mathematicians
The course is designed for anyone interested in learning Linear Algebra for use in Data Analysis, Data Science, Artificial Intelligence, Machine Learning, Deep Learning, Computer Graphics, and Programming.
Top 3 reviewed by the buyer
1- It was a great experience to learn this course and I had a lot of fun doing it. There is a lot of clarity in Kashif’s explanations and examples. As a result of this course, I developed an intuition about a lot of concepts that I didn’t know much about before. It is my pleasure to recommend this course and Kashif as an instructor to you. There were many questions that I asked in the forum, and he always made sure to answer them in an engaging way so that I understood what he was saying. My one recommendation for improving the learning process would be to provide detailed solutions to the assignments instead of just providing the final answer as a result of the assignment. Once again, thank you very much!
2- It is a really good course for people who are just starting out. The explanations of a few topics have been given using variables (like x, y, a, b…) which is good, but if you were to then give examples with real numbers then it would be more useful for the students to understand the topics in a more clear manner. The fact that you can explain why we are doing something and what we are going to achieve with that topic will make all the topics seem much more structured and you will get 5 stars from all the students, I’m sure.
3- It was a solid course that covered a lot of material. This was not quite what I was expecting, especially when it came to the machine learning part (it had almost nothing to do with ML). There was only one topic covered in this class and it was linear algebra, which is “used” in both DS and ML. It’s not really about anything related to DS or ML. But, despite the fact that it was a good presentation, it was extremely knowledgeable as well. There felt like he repeated a lot of things, so I think it was longer than it should have been because he repeated a lot of things. That may be good practice for those who are new to linear algebra, especially if they are complete novices in this area.
Number 4. Linear Algebra for Beginners: Open Doors to Great Careers
What you’ll learn
It is time to refresh your math knowledge.
Develop a solid foundation in Linear Algebra in order to further your career as a mathematician.
Learn one of the mathematical subjects that are crucial to the study of computer science.
Take a class in one of the mathematical subjects that are essential for engineering, computer science, physics, economics, computer animation, and cryptography among many others.
Take a course in one of the mathematical subjects that are required for Data Science.
It is important to learn a mathematical subject that will help you become a Quant on Wall Street.
Who this course is for:
Professionals in the working world
This course is for anyone who is interested in gaining a thorough understanding of the core concepts of Linear Algebra.
Learners who are adults
Students in college
Top 3 reviewed by the buyer
1- There is no doubt that the course is quite excellent. It is not only the subject matter that is covered in this lecture, but the lecturer also explains the terminology and relates the concepts to the subject matter covered in the previous lectures. There is also an introduction to the required mathematical symbols by the lecturer as needed. I must say that you have done an excellent job again.
Pros:
The explanation is very straightforward and concise, which is very helpful for someone who would like to learn linear algebra very quickly. As a result, you can save a lot of time and effort. In spite of this, the course does not miss the essence of what it is all about.
Cons:
It is necessary to increase the speed of the video to match the speed of the lecture; the speed of the lecture is slow.
There is a lack of quality in the presentation, as the lecturer writes everything and draws it all by hand one at a time.
2- As a result of the fact that it is quite abstract, it might be a little difficult for beginners to understand some of the concepts. It is recommended that you take this course along with another intuitive course at the same time. That is the best combination you can get. I have bought his second linear algebra course as well, and I am very satisfied with it as well.
3- In the past, I took a linear algebra course, and I was looking for a refresher on the topic since I took it many years ago. I found this course to be perfect for me since I could speed up the course very quickly to get through the things I already understood very quickly, while I was able to spend more time on areas that needed more attention. It was also very helpful to have ample problem sets in addition to the course material, as it enhanced the learning process.
Number 5. College Level Advanced Linear Algebra! Theory & Programming!
What you’ll learn from this algebra course
This course will give you a deeper understanding of linear algebra from a theoretical, conceptual, and practical perspective.
The goal of this program is to provide a very robust mathematical foundation for machine learning, deep learning, computer graphics, and control systems.
You will be able to learn how to solve & visualize Linear Algebra problems using both Python and Matlab.
You will learn how to differentiate and optimize complex equations involving matrices in this course on matrix calculus.
Discover how linear regression works, how the normal equation works, and what the projection matrix is.
You can learn about Singular Value decomposition from a formal and conceptual perspective.
You can learn about Inverses and Pseudo Inverses by watching the video below.
Those who are interested in taking this course are:
It will be of interest to anyone who is interested in linear algebra, especially in the context of computer engineering, computer science, or data science, but not limited to those contexts.
Those who are interested in machine learning and deep learning are welcome to apply.
Those who are interested in computer graphics and game development are welcome to attend.
Those who are interested in classical control systems and robotics are welcome to attend.
The use of Python and Matlab for Linear Algebra is something that a lot of people are interested in learning how to do.
This book is intended for anyone who is interested in Linear Algebra Theory, Concepts, and Proofs.
Top 3 reviewed by the buyer
1- I have completed Deep Learning courses one and two at the college level, and I found them to be one of the most interesting and refreshing courses I have taken. I would like to suggest that the most important thing you could do for the series is to expand it by including unsupervised learning components as well, especially generative models, and if you could also include transformers in there as well, that would be great as well. My friend, it’s been a great learning experience so far, and I am looking forward to learning more in the future from the instructor. Even in top-tier universities, they don’t cover concepts like the instructor does, for example, they ask us to memorize matrix cookery by heart, but not in this course. This doesn’t even have to be done by me, I was going through a paper review by Yannick Kilcher and he happened upon a seemingly simple linear algebra part of a famous paper that had been overlooked. According to AlexNet, he couldn’t explain how it was explained, but I was able to understand it only because I took your linear algebra course. Keep up the good work and I hope to see more of it in the future.
2- It was a very useful course! As a result, I gained a deeper understanding of the concept of matrices and matrix calculus! There is no doubt that every student studying deep learning should watch this course! It is a long course, so please take your time to complete it as it is a challenging one!
3- This course is still awesome to me even after rewatching it for the second time! It is really very nice and I really like it! The course covers a wide range of advanced topics that are generally not covered in Linear Algebra courses, including matrix calculus and systems of differential equations. There is also a lot I like about the Python / Matlab programming and visualization aspects of the project.
Discover More Course — 5 Best Courses for Business Analysts
Conclusion
Many fields use linear algebra, including computer science, physics, and engineering. You can learn linear algebra from these five courses if you’re interested. From the basics to the more advanced topics, every course will give you the skills you need to solve complex problems. Whether you’re a beginner or an advanced learner, we have a course for you. If you put in the effort, you can master linear algebra and unlock a world of opportunities. Take one of these linear algebra courses now and start mastering it!
