COMPLETE LINEAR ALGEBRA THEORY AND IMPLEMENTATION

Learn concepts in linear algebra and matrix analysis, and implement them in MATLAB and Python.
Bestseller
Created by Mike X Cohen
Last updated 4/2019
English

What you’ll learn
Understand theoretical concepts in algebra , including proofs
Implement algebra concepts in scientific programming languages (MATLAB, Python)
Apply algebra concepts to real datasets
Ace your algebra exam!
Apply algebra on computers confidently
Gain additional insights into solving problems in algebra , including homeworks and applications
Be confident in learning advanced algebra topics
Understand a number of the important maths underlying machine learning
* Manually corrected closed-captions *

Requirements
Basic understanding of high-school algebra (e.g., solve for x in 2x=5)
Interest in learning about matrices and vectors!
(optional) Computer with MATLAB, Octave, or Python (or Jupyter)

Description
You need to find out linear algebra!
Linear algebra is probably the foremost important branch of mathematics for computational sciences, including machine learning, AI, data science, statistics, simulations, special effects , multivariate analyses, matrix decompositions, and so on. You need to understand applied algebra , not just abstract linear algebra! The way algebra is presented in 30-year-old textbooks is different from how professionals use algebra in computers to unravel real-world applications. for instance , the “determinant” of a matrix is vital for algebra theory, but do you have to actually use the determinant in practical applications? the solution may surprise you, and it’s during this course! If you’re curious about learning the mathematical concepts algebra and matrix analysis, but also want to use those concepts to data analyses on computers, then this course is for you! Unique aspects of this course
Clear and comprehensible explanations of concepts and theories in algebra .
Several distinct explanations of an equivalent ideas, which may be a proven technique for learning.
Visualization using graphs, numbers, and spaces that strengthens the geometric intuition of algebra .
Implementations in MATLAB and Python. Com’on, within the world , you never solve math problems by hand! you would like to understand the way to implement math in software!
Beginning to intermediate topics, including vectors, matrix multiplications, least-squares projections, eigendecomposition, and singular-value decomposition.
Strong specialise in modern applications-oriented aspects of algebra and matrix analysis.
Intuitive visual explanations of diagonalization, eigenvalues and eigenvectors, and singular value decomposition.
Benefits of learning algebra Understand statistics including least-squares, regression, and multivariate analyses.
Improve simulations in engineering, computational biology, finance, and physics.
Understand data compression and dimension-reduction (PCA, SVD, eigendecomposition).
Understand the maths underlying machine learning and linear classification algorithms.
Explore the link between algebra , matrices, and geometry.

Why i’m qualified to show this course:
I have been using algebra extensively in my research and teaching (primarily in MATLAB) for several years. I even have written several textbooks about data analysis, programming, and statistics, that rely extensively on concepts in algebra .

So what are you waiting for??
Watch the course introductory video and free sample videos to find out more about the contents of this course and about my teaching style. If you’re unsure if this course is true for you and need to find out more, be happy to contact with me questions before you check in .

I hope to ascertain you soon within 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!
Course content
all 152 lectures 21:02:49
Size: 6.46G

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