Complete linear algebra: theory and implementation in code Free Download

Linear algebra is the branch of mathematics that describes linear relations between vectors, scalars, and matrices. It underlies many aspects of mathematics and science including differential and integral calculus, Euclidean geometry, polynomials, differential equations, linear programming, vector calculus. Although a deeper study of linear algebra is beyond the scope of this primer course it will provide a working model for a beginner to follow easily.

What you’ll learn in Complete linear algebra: theory and implementation in code

  1. Understand linear algebra’s theoretical concepts, including proofs.
  2. Use scientific programming languages (MATLAB and Python) to implement linear algebra concepts.
  3. Use real-world datasets to practice linear algebra.
  4. Your linear algebra exam will be a breeze to pass!
  5. With confidence, use linear algebra on a computer
  6. Learn more about how to solve problems in linear algebra by doing homework and applying what you’ve learned.
  7. Learn advanced linear algebra topics with confidence.
  8. Recognize some of the key mathematical concepts that underpin machine learning.
  9. Most AI (artificial intelligence) is based on mathematics.

Requirements

  • Basic algebra skills (e.g., solving for x in 2x=5) are required.
  • Matrix and vectors piqued your interest!
  • (optional) MATLAB, Octave, or Python (or Jupyter) on a computer

Description

Linear algebra is a must-know for you!
Machine learning, AI, data science, statistics, simulations, computer graphics, multivariate analyses, matrix decompositions, signal processing, and other computational sciences rely heavily on linear algebra.

The way linear algebra is presented in 30-year-old textbooks differs 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 “determinant” of a matrix is important for linear algebra theory, but should you use it in practice? The answer may surprise you, and it’s in this course!

The course’s distinct features
Linear algebra has a lot of advantages.
I am qualified to teach this course because of the following factors:
For many years, I have used linear algebra extensively in my research and teaching (in MATLAB and Python) and have written several textbooks on data analysis, programming, and statistics that heavily rely on linear algebra concepts.

To learn more about the contents of this course and my teaching style, watch the course introductory video and free sample videos; if you’re not sure if this course is right for you and want to learn more, contact me with your questions before you sign up.

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
File Name :Complete linear algebra: theory and implementation in code Free Download
Content Source:udemy
Genre / Category:Teaching & Academics
File Size :2.60 gb
Publisher :Mike X Cohen
Updated and Published:24 Oct,2021

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