College Level Advanced Linear Algebra! Theory & Programming!
What you'll learn
- Gain Deep Understanding Of Linear Algebra Theoretically, Conceptually & Practically.
- Obtain A Very Robust Mathematical Foundation For Machine & Deep Learning, Computer Graphics, And Control Systems.
- Learn How To Use Both Python And Matlab For Solving & Visualizing Linear Algebra Problems.
- [Matrix Calculus] Learn How To Differentiate & Optimize Complex Equations Involving Matrices.
- Learn A Lot About Data Science, Co-variance Matrices, And The PCA.
- Learn About Linear Regression, The Normal Equation, And The Projection Matrix.
- Learn About Singular Value Decompositions Formally & Conceptually.
- Learn About Inverses And Pseudo Inverses.
- Learn About Determinants And Positive Definite Matrices.
- Learn How To Solve Systems Of Linear, Difference, & Differential Equations Both By Hand And Software.
- Learn About Lagrange Multipliers & Taylor Expansion.
- Learn About The Hessian Matrix And Its Importance In Multi-variable Calculus & Optimizations.
- Learn About Complex Transformation Matrices Like The Matrix To Perform Rotation Around An Arbitrary Axis In 3D.
- And Much More ! This is a 34+ hours course !
Requirements
- Be familiar with Linear Algebra basics such as Vectors, Matrices, Dot Products, Cross Products, and Systems Of Linear Equations.
- For some of the advanced topics presented, I might require you to know some specific topics like partial differentiation or Laplace transform. However, you can easily skip those topics and still completely understand the subsequent sections.
Description
From Matrix Calculus, To Robotics! From Control Systems, To Computer Graphics! From the Singular Value Decompositions to the Principal Component Analysis. From Systems Of Linear Equations, To Systems Of Differential Equations. From Inverses, to Pseudo Inverses. From Determinants, to positive definiteness. From Concepts To Programming. From Matlab To Python. From Proofs to Visualizations & From Theory to Applications. From Solved Examples To thoughtful Exams, and From Many Other Things to Many other things,
I, Present This Course !
My Name is Ahmed Fathy, currently a machine learning scientist at Affectiva, and a university teacher previously. Over the years, I happened to teach many subjects that make a very deep use of linear algebra. Those include Machine Learning and Deep Learning, Computer Graphics, Control Systems, Game Development, and even Pure Linear Algebra. Every one of those subjects handled linear algebra from very different perspectives. In this course, I provide them all.
This course is intended to be a Reference on linear algebra in the world of online courses, having proofs, theories, programming, concepts, applications, solved examples, visualizations, and everything ! Any suggestions for more topics to add are always welcome. Since the course contents are so large and extensive, I will not summarize them here. Instead, I ask you to please watch the promo video & also have a look on the course contents towards the bottom of the page. Have a nice day !
Who this course is for:
- Anyone Interested In Linear Algebra, especially, but not limited to, in the context of computer engineering, computer science, or data-science.
- Anyone Interested In Machine Learning & Deep learning.
- Anyone Interested In Computer Graphics & Game Development.
- Anyone Interested In Classical Control Systems & Robotics.
- Anyone Interested To know how to use python & matlab for Linear Algebra.
- Anyone Interested In Linear Algebra Theories, Concepts, And Proofs.
Instructor
![Ahmed Fathy, MSc](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.4 Instructor Rating
- 819 Reviews
- 39,185 Students
- 6 Courses
My name is Ahmed Fathy. I have a master's degree in deep-learning, and I am currently a machine learning scientist at Affectiva, applying deep-learning multimodal techniques to human emotion recognition, and previously a teaching assistant at Ain-Shams University, Cairo, Egypt. On Campus, I used to teach highly variant subjects to students, including Deep Learning, Pattern Recognition, Computer Graphics, Computer Architecture, Basic and advanced programming, Linear Algebra, Design Patterns, Compilers, Control Systems, and Game Development.