Master Linear Algebra: From Theory to Implementation
Original price was: $19.99.$14.99Current price is: $14.99.
What youâll learn
-
Understand theoretical concepts in linear algebra, including proofs
-
Implement linear algebra concepts in scientific programming languages (MATLAB, Python)
-
Apply linear algebra concepts to real datasets
-
Ace your linear algebra exam!
-
Apply linear algebra on computers with confidence
-
Gain additional insights into solving problems in linear algebra, including homeworks and applications
-
Be confident in learning advanced linear algebra topics
-
Understand some of the important maths underlying machine learning
-
The math underlying most of AI (artificial intelligence)
You need to learn linear algebra!
Linear algebra is perhaps the most important branch of mathematics for computational sciences, including machine learning, AI, data science, statistics, simulations, computer graphics, multivariate analyses, matrix decompositions, signal processing, and so on.
You need to know applied linear algebra, not just abstract linear algebra!
The way linear algebra is presented in 30-year-old textbooks is different from how professionals use linear algebra in computers to solve real-world applications in machine learning, data science, statistics, and signal processing. For example, the âdeterminantâ of a matrix is important for linear algebra theory, but should you actually use the determinant in practical applications? The answer may surprise you, and itâs in this course!
If you are interested in learning the mathematical concepts linear algebra and matrix analysis, but also want to apply those concepts to data analyses on computers (e.g., statistics or signal processing), then this course is for you! Youâll see all the maths concepts implemented in MATLAB and in Python.
Unique aspects of this course
-
Clear and comprehensible explanations of concepts and theories in linear algebra.
-
Several distinct explanations of the same ideas, which is a proven technique for learning.
-
Visualization using graphs, numbers, and spaces that strengthens the geometric intuition of linear algebra.
-
Implementations in MATLAB and Python. Comâon, in the real world, you never solve math problems by hand! You need to know how to implement math in software!
-
Beginning to intermediate topics, including vectors, matrix multiplications, least-squares projections, eigendecomposition, and singular-value decomposition.
-
Strong focus on modern applications-oriented aspects of linear algebra and matrix analysis.
-
Intuitive visual explanations of diagonalization, eigenvalues and eigenvectors, and singular value decomposition.
-
Improve your coding skills! You do need to have a little bit of coding experience for this course (I do not teach elementary Python or MATLAB), but you will definitely improve your scientific and data analysis programming skills in this course. Everything is explained in MATLAB and in Python (mostly using numpy and matplotlib; also sympy and scipy and some other relevant toolboxes).
Benefits of learning linear algebra
-
Understand statistics including least-squares, regression, and multivariate analyses.
-
Improve mathematical simulations in engineering, computational biology, finance, and physics.
-
Understand data compression and dimension-reduction (PCA, SVD, eigendecomposition).
-
Understand the math underlying machine learning and linear classification algorithms.
-
Deeper knowledge of signal processing methods, particularly filtering and multivariate subspace methods.
-
Explore the link between linear algebra, matrices, and geometry.
-
Gain more experience implementing math and understanding machine-learning concepts in Python and MATLAB.
-
Linear algebra is a prerequisite of machine learning and artificial intelligence (A.I.).
Why IÂ am qualified to teach this course:
I have been using linear algebra extensively in my research and teaching (in MATLAB and Python) for many years. I have written several textbooks about data analysis, programming, and statistics, that rely extensively on concepts in linear algebra.Â
So what are you waiting for??
Watch the course introductory video and free sample videos to learn more about the contents of this course and about my teaching style. If you are unsure if this course is right for you and want to learn more, feel free to contact with me questions before you sign up.
IÂ hope to see you soon in the course!
Mike
Who this course is for:
- Anyone interested in learning about matrices and vectors
- Students who want supplemental instruction/practice for a linear algebra course
- Engineers who want to refresh their knowledge of matrices and decompositions
- Biologists who want to learn more about the math behind computational biology
- Data scientists (linear algebra is everywhere in data science!)
- Statisticians
- Someone who wants to know the important math underlying machine learning
- Someone who studied theoretical linear algebra and who wants to implement concepts in computers
- Computational scientists (statistics, biological, engineering, neuroscience, psychology, physics, etc.)
- Someone who wants to learn about eigendecomposition, diagonalization, and singular value decomposition!
- Artificial intelligence students
12 reviews for Master Linear Algebra: From Theory to Implementation
Add a review
Original price was: $19.99.$14.99Current price is: $14.99.
Alvin_oy Zhang –
This is a spectacular course which simplifies fundamentals of linear algebra to a small number of plain simple functions that showcase the power and beauty of this subject, it’s the best primer.
Joseph Cannella –
Great course. I had never taken a full course on Linear Algebra and it kept coming up for my work and continued studies,. Really intuitive explanations and the math is worked out in detail. Learned several techniques that are immediately applicable to my work.
KĂ˝ Quáťc Viáťt –
There are many detail explanation about the formulas and visualization that help you remember their properties and concepts easily. But I feel like it lacks of real world applications per sections. I understand these applications could be in another course but some small demonstrations will be more helpful to give us the motivation to move on.
James Mann –
Probably the best course I’ve taken on Udemy in terms of accountability in learning and retaining the course material. The frequent code challenges and quizzes force you use what has just been taught if even in a simple example.
I would recommend picking up the textbook from the same author “Linear Algebra: Theory, Intuition, Code” if you want a reference source. I find skimming through videos to be an extremely slow method of finding relevant material when you want to go back and review something but a book is quick and an e-book is even better.
J.Hong H –
Thanks to Mike’s idea to combine math with coding, I’m more confident on linear algebra and on its application when I start to learn data-related computer techniques in the future.
Luca Introzzi –
This is not just another course in Linear Algebra, this is by far the best one I’ve ever attended. Actually much better than analogous courses I attended in Physics and Psychology.
It helped building my intuition and my code expertise step by step, and it has been really useful for my thesis. And now I have a finer and deeper grasp of the topic.
Thanks!
Phong Nguyen –
The course provide very essential knowledge of linear algebra
Ashish Sharma –
It is an outstanding course for sure. Dr Mike is truly and incredible instructor. This is how maths should be taught!
I can list a couple of benefits one would get on enrolling in this course
*he is a true expert & genius in his domain.
*you would get an opportunity to see the material as someone proficient in the field does, with all the connections between different concepts and understanding of each. An insight into the mental model of an expert for the subject.
*combining coding/visualizations with mathematical concepts is Super powerful. Geometric perspective of LA concepts are amazing. We all study those equations and work with them but learning this way will give you an unmatched understanding of it. You will see how they are applied instead of just some equation in your notebook/textbook
*it builds up slowly from ground up and he explains even the most difficult topics in a way that you understand(one feature of a great teacher)
*it might not seem much right now but the slides are Beautifully made with color coding which aids your studying
*he is responsive when you ask a question on the Q&A unlike some instructors, which helps clear your doubts if you get stuck or don’t understand something within the right time.
*of all, he is a good & helpful person overall & will guide you through the complete learning process
Thanks for this Amazing course Sir!
Ryan Antonio –
After finishing this course, I can tell you that it has been a blast! I have three key things to say about it:
1. This course is well authored because of the progressive outline. The concepts were well-divided into bite-size lectures and exercises. It’s as if every discussion is a component or layer of your entire linear algebra matrix hehe đ
2. There are several exercises! Both hand-written and lab works help in fortifying your understanding of every concept discussed.
3. Every bit of this course matters! In the beginning, every fundamental theory is tackled. Near the end, you’ll see that all these theories converge into several cool concepts. My personal favorite was everything about the different ways to decompose your matrix into other matrices. Also, on how each component says something about the matrix being analyzed.
Overall, I’d give this 6/5 stars (I wish there’s a way to make that happen). Thank you, professor Mike! I wish you were my professor when I was still an undergrad.
P.S. For those who might see this comment, I guarantee that everything in this course builds a strong foundation in both theory and practice of linear algebra.
S.Wang –
My first MATH course on Udemy. Watch the entire course twice. A treasure on Udemy. I also bought an introductory linear algebra book from another author. This course is really good complement of that book.
To me, mathematics is a rigorous science which means formal definitions, propositions and proofs are hand in hand with math concepts. But those books with formal proofs somewhat lack of applications and scientific computing(coding) sections. The lecturer teaches linear algebra from a different perspective. Recommended! Thank you Mike!
Stephen Link –
TLDR version – Solid coverage of the basics but needs practical examples to move from theory into real-world application.
Long Version – First comes the good part. This instructor really lays a solid foundation early in the course to help you grasp the fundamentals. He also goes into much greater detail on Linear Algebra concepts, and covers more concepts, than other courses I’ve seen (Udacity, I’m looking at you here…)
Now for the not so good part. I had gotten the impression from the course description that this was more of an “applied” approach rather than straight theory. I don’t feel that the course delivers on this. You’re often told that this particular concept is used in that particular field, but you never really see this in action. Some actual examples, with datasets that look at least somewhat real, would really help bring this theory down into real-life usage. Some examples come closer to this than others, but at no point did I feel that I saw something worked out in such a way that I could take it and directly apply it. This seems to get worse the further you go into the course and by the final two or three sections I felt like I was getting straight theory.
There are many code examples used as teaching aids. This is fine, and expected, given the course introduction. But, you need to be sure that you can follow Matlab or Python code to get any benefit from these examples. I can read/write Python so I was OK, but I didn’t see anything in the description stating that programming knowledge was required. I think that someone who lacked that knowledge would struggle to follow many lessons, and they would certainly struggle with the (many) code challenges in this course.
Gerard Mattei –
Arguably, the best course I have ever taken. The decision to mix math with programming to build muscle memory/conceptual understanding was excellent.