Linear Algebra for Data Science, Big Data, Machine Learning, Engineering & Computer Science. Master Linear Algebra
- A passion to learn about Matrices and Vectors
- Ability to perform basic Mathematical operations (+, -, x, ÷) on numbers and fractions
- Knowledge of how to solve a linear equation (e.g. find x in 3x-4=11)
- Understanding of basic Algebra concepts, e.g. Powers and Roots, simplifying Fractions, Factorization, solving Equations and drawing Graphs.
- You only need to know basic Math and Algebra to take this course.
- And the best thing is, most of the above prerequisite topics are covered inside the course 🙂
DO YOU WANT TO LEARN LINEAR ALGEBRA IN AN EASY WAY?
With 22+ hours of content and 200+ video lessons, this course covers everything in Linear Algebra, from start till the end!
Every concept is explained in simple language, and Quizzes and Assignments (with solutions!) help you test your concepts as you proceed.
Whether you’re a student, or a professional or a Math enthusiast, this course walks you through the core concepts of Linear Algebra in an easy and fun way!
HERE IS WHAT YOU WILL LEARN:
· Fundamentals of Linear Algebra and how to ace your Linear Algebra exam
· Basics of matrices, including notation, dimensions, types, addressing the entries etc.
· Operations on a single matrix, e.g. scalar multiplication, transpose, determinant, adjoint etc.
· Operations on two matrices, including addition, subtraction and multiplication
· Performing elementary row operations and finding Echelon Forms (REF & RREF)
· Inverses, including invertible and singular matrices, and the Cofactor method
· Solving systems of equations using matrices & inverse matrices, including Cramer’s rule to solve AX = B
· Performing Gauss-Jordan elimination
· Properties of determinants and how to utilize them to gain insights
· Matrices as vectors, including vector addition and subtraction, Head-to-Tail rule, components, magnitude and midpoint of a vector
· Linear combinations of vectors and span
· Vector spaces, including dimensions, Euclidean spaces, closure properties and axioms
· Subspace and Null-space of a matrix, matrix-vector products
· Spanning set for a vector space and linear dependence
· Basis and standard basis, and checking if a set of given vectors forms the basis for a vector space
· Eigenvalues and Eigenvectors, including how to find Eigenvalues and the corresponding Eigenvectors
· Basic algebra concepts (as a BONUS)
· And so much more…..
HERE IS WHAT YOU GET IN THE COURSE:
Video Lessons: Watch over my shoulder as I explain all the Linear Algebra concepts in a simple and easy to understand language. Everything is taught from scratch, and no prior knowledge is assumed.
Solved Examples: Every topic is explained with the help of solved examples, from start to end. This problem-based approach is great, especially for beginners who want to practice their Math concepts while learning.
Quizzes: When you think you have understood a concept well, test it by taking the relevant quiz. If you pass, awesome! Otherwise review the suggested lessons and retake the quiz, or ask for help in the Q/A section.
Assignments: Multiple assignments offer you a chance for additional practice by solving sets of relevant and insightful problems (with solutions provided)
By the end of this course, you’ll feel confident and comfortable with all the Linear Algebra topics discussed in this course!
WHY SHOULD YOU LEARN LINEAR ALGEBRA?
· Linear Algebra is a prerequisite for many lucrative careers, including Data Science, Artificial Intelligence, Machine Learning, Financial Math, Data Engineering etc.
· Being proficient in Linear Algebra will open doors for you to many high-in-demand careers
WHY LEARN LINEAR ALGEBRA FROM ME?
I took this Linear Algebra class at University of Illinois at Urbana Champaign, one of the Top-5 Engineering Schools in the country, and I have tried to follow the same standards while designing this course.
I have taught various Math and Engineering courses for more than 10 years at schools across US, Asia and Africa. I strongly believe that I have the ability to breakdown complex concepts into easily understandable chunks of information for you!
I provide premium support for all my students – so if you ever get stuck or have a question, just post it to the course dashboard and I’ll be there to help you out in a prompt and friendly way!
My goal is to make this the best Linear Algebra and Math course online, and I’ll do anything possible to help you learn.
HERE IS WHAT STUDENTS SAY ABOUT THIS COURSE:
“I thoroughly enjoyed this course. I needed to get a better understanding and a good base of Linear Algebra for Data Science and Machine Learning and Kashif absolutely delivered. This is definitely a Zero to Hero course on Linear Algebra in my opinion, and would highly recommend this to anyone who is on the same path as I am. Nothing but appreciation for this author.” – I. Valderrama
“Wish I had found this earlier” – Dan
“Great explanations. Solid teaching” – J. P. Baugh
“Excellent course! The course material is really good, explanation is really clear and every new concept is provided with examples that make the experience even better! The instructor always takes the time to answer questions poster in Q&A. New material is constantly added to course. Thank you!” – K. Geagea
YOU’LL ALSO GET:
· Lifetime access to “Complete Linear Algebra for Data Science & Machine Learning”
· Friendly support in the Q&A section
· Udemy Certificate of Completion available for download
· 30-day, no-questions-asked, money back guarantee
Feel free to check out the course outline below or watch the free preview lessons. Or go ahead and enroll now.
I can’t wait for you to get started with Linear Algebra.
Who this course is for:
- Students enrolled or planning to enroll in Linear Algebra class, and who want to excel in it
- Professionals who need a refresher in Math, especially Algebra and Linear Algebra
- Engineers, Scientists and Mathematicians who want to work with Linear Systems and Vector Spaces
- Anyone who wants to master Linear Algebra for Data Science, Data Analysis, Artificial Intelligence, Machine Learning, Deep Learning, Computer Graphics, Programming etc.