Practical Linear Algebra

For aspiring Data scientists and Core Engineers

What you'll learn
Gain graphical and physical understanding of Determinant (graphical perspective), Inverse (graphical perspective), Linear independence & dependence,
Gain graphical and physical understanding of Simultaneous equations, Eigenvalue & Eigenvectors, Linear transformation, Vector & Tensor transformation
Foundational linear algebra for data science, machine learning, computer vision.
Conceptually it covers the engineering curriculum of linear algebra.

Requirements
No programming knowledge needed
Description
Linear algebra is fundamental and central to many branches of mathematics and is highly relevant to current sciences such as data science, machine learning and more…

The course is fundamentally designed for aspiring core engineers and data scientists, incepting from the grass root level, and is discussed in the context of engineering. The unique feature of this course is that mathematical ideas are narrated via graphical animation. This unique feature helps provide highest clarity on mathematical ideas and builds graphical intuition.

Levels of Practical Linear Algebra

* Fundamentals of linear algebra

* GATE Preparations

* Higher Order Thinking

* Building Research Aptitude

Key Subject Take-aways

Gain graphical and physical understanding of concepts such as

* Determinant (graphical perspective)

* Inverse (graphical perspective)

* Linear independence & dependence

* Simultaneous equations

* Eigenvalue & Eigenvectors

* Linear transformation

* Vector transformation

* Tensor transformation

Practical Take-aways

* Complete preparation for GATE-Mathematics.

* Foundational linear algebra for data science, machine learning, computer vision.

* Conceptually it covers the engineering curriculum of linear algebra.

Advanced Discussions

* Multiple perspectives to circle to ellipse transformation.

* Detailed understanding of Eigen decomposition.

* Coordinate transformation of engineering tensors.

HOT and Research Aptitude

HOT and research aptitude sections emphasise on vector and tensor transformation which is fundamental to computer graphics. The idea of transformation is relevant to even computer vision.

We recommend this course to young engineers who really want to apply linear algebra to engineering situation

Who this course is for
Undergraduates and those preparing for competitive exams
Those who want to take up assignments in machine learning /data science. Math enthusiasts
Math faculties who want to innovate and teach with engineering relevance.